references.bib

@INCOLLECTION{Rodenburg2008-ww,
  title     = "Ptychography and Related Diffractive Imaging Methods",
  booktitle = "Advances in Imaging and Electron Physics",
  author    = "Rodenburg, J M",
  editor    = "{Hawkes}",
  abstract  = "Publisher Summary Ptychography is a nonholographic solution of
               the phase problem. It is a method for calculating the phase
               relationships among different parts of a scattered wave
               disturbance in a situation where only the magnitude (intensity
               or flux) of the wave can be physically measured. Its usefulness
               lies in its ability (like holography) to obtain images without
               the use of lenses, and hence to lead to resolution improvements
               and access to properties of the scattering medium that cannot be
               easily obtained from conventional imaging methods. The chapter
               discusses ptychography in the context of other phase-retrieval
               methods in both historical and conceptual terms. In an original
               and oblique approach to the phase problem, it was Hoppe who
               proposed the first version of the particular solution to the
               phase problem. The word ``ptychography'' was introduced to
               suggest a solution to the phase problem using the convolution
               theorem, or rather the ``folding'' of diffraction orders into
               one another via the convolution of the Fourier transform of a
               localized aperture or illumination function in the object plane.
               Apart from computers, the two most important experimental issues
               that affect ptychography, relate to the degree of coherence in
               the illuminating beam and the detector efficiency and dynamic
               range.",
  publisher = "Elsevier",
  volume    =  150,
  pages     = "87--184",
  month     =  jan,
  year      =  2008
}


@ARTICLE{Rodenburg2004-oi,
  title     = "A phase retrieval algorithm for shifting illumination",
  author    = "Rodenburg, J M and Faulkner, H M L",
  abstract  = "We propose a method of iterative phase retrieval that uses
               measured intensities in the diffraction plane to solve the phase
               problem in a way that bypasses the problem of lens aberration,
               leading to greatly improved spatial resolution. This method is
               stable, easy to implement experimentally, and can be used to
               view a large area of the specimen when that is desired.",
  journal   = "Appl. Phys. Lett.",
  publisher = "American Institute of Physics",
  volume    =  85,
  number    =  20,
  pages     = "4795--4797",
  month     =  nov,
  year      =  2004
}


@ARTICLE{Maiden2009-pn,
  title    = "An improved ptychographical phase retrieval algorithm for
              diffractive imaging",
  author   = "Maiden, Andrew M and Rodenburg, John M",
  abstract = "The ptychographical iterative engine (or PIE) is a recently
              developed phase retrieval algorithm that employs a series of
              diffraction patterns recorded as a known illumination function is
              translated to a set of overlapping positions relative to a target
              sample. The technique has been demonstrated successfully at
              optical and X-ray wavelengths and has been shown to be robust to
              detector noise and to converge considerably faster than
              support-based phase retrieval methods. In this paper, the PIE is
              extended so that the requirement for an accurate model of the
              illumination function is removed.",
  journal  = "Ultramicroscopy",
  volume   =  109,
  number   =  10,
  pages    = "1256--1262",
  month    =  sep,
  year     =  2009,
  language = "en"
}


@ARTICLE{Popoff2009-xs,
  title         = "Measuring the Transmission Matrix in Optics : An Approach to
                   the Study and Control of Light Propagation in Disordered
                   Media",
  author        = "Popoff, S M and Lerosey, G and Carminati, R and Fink, M and
                   Boccara, A C and Gigan, S",
  abstract      = "We introduce a method to experimentally measure the
                   monochromatic transmission matrix of a complex medium in
                   optics. This method is based on a spatial phase modulator
                   together with a full-field interferometric measurement on a
                   camera. We determine the transmission matrix of a thick
                   random scattering sample. We show that this matrix exhibits
                   statistical properties in good agreement with random matrix
                   theory and allows light focusing and imaging through the
                   random medium. This method might give important insights
                   into the mesoscopic properties of complex medium.",
  month         =  oct,
  year          =  2009,
  archivePrefix = "arXiv",
  primaryClass  = "physics.optics",
  eprint        = "0910.5436"
}

 @article{Pai_Bosch_Mosk_2018, title={Transmission Matrix measurement procedure and analysis}, author={Pai, Pritam and Bosch, Jeroen and Mosk, Allard}, year={2018} }
 
 
@ARTICLE{Huang2014-yo,
  title    = "Optimization of overlap uniformness for ptychography",
  author   = "Huang, Xiaojing and Yan, Hanfei and Harder, Ross and Hwu,
              Yeukuang and Robinson, Ian K and Chu, Yong S",
  abstract = "We demonstrate the advantages of imaging with ptychography scans
              that follow a Fermat spiral trajectory. This scan pattern
              provides a more uniform coverage and a higher overlap ratio with
              the same number of scan points over the same area than the
              presently used mesh and concentric [13] patterns. Under
              realistically imperfect measurement conditions, numerical
              simulations show that the quality of the reconstructed image is
              improved significantly with a Fermat spiral compared with a
              concentric scan pattern. The result is confirmed by the
              performance enhancement with experimental data, especially under
              low-overlap conditions. These results suggest that the Fermat
              spiral pattern increases the quality of the reconstructed image
              and tolerance to data with imperfections.",
  journal  = "Opt. Express",
  volume   =  22,
  number   =  10,
  pages    = "12634--12644",
  month    =  may,
  year     =  2014,
  language = "en"
}

@ARTICLE{Maiden2010-un,
   title    = "Optical ptychography: a practical implementation with useful
               resolution",
   author   = "Maiden, Andrew M and Rodenburg, John M and Humphry, Martin J",
   abstract = "Quantitative phase microscopy offers a range of benefits over
               conventional phase-contrast techniques. For example, changes in
               refractive index and specimen thickness can be extrapolated and
               images can be refocused subsequent to their recording. In this
               Letter, we detail a lensless, quantitative phase microscope with
               a wide field of view and a useful resolution. The microscope uses
               the recently reported coherent diffractive imaging technique of
               ptychography to generate images from recorded diffraction
               patterns.",
   journal  = "Opt. Lett.",
   volume   =  35,
   number   =  15,
   pages    = "2585--2587",
   month    =  aug,
   year     =  2010,
   language = "en"
 }
 
 
@PHDTHESIS{Bouchet2017-hk,
  title    = "Plasmon-mediated energy transfer and super-resolution imaging in
              the near field of nanostructured materials",
  author   = "Bouchet, Dorian",
  abstract = "PDF | In this thesis, we perform experimental measurements and
              data modelling to investigate spontaneous emission of fluorescent
              emitters in nanostructured environments. The manuscript is
              organised into two main parts.In the first part, we study
              micrometre-range energy transfer...",
  month    =  nov,
  year     =  2017
}



@ARTICLE{Maiden2013-zp,
  title    = "Soft X-ray spectromicroscopy using ptychography with randomly
              phased illumination",
  author   = "Maiden, A M and Morrison, G R and Kaulich, B and Gianoncelli, A
              and Rodenburg, J M",
  abstract = "Ptychography is a form of scanning diffractive imaging that can
              successfully retrieve the modulus and phase of both the sample
              transmission function and the illuminating probe. An experimental
              difficulty commonly encountered in diffractive imaging is the
              large dynamic range of the diffraction data. Here we report a
              novel ptychographic experiment using a randomly phased X-ray
              probe to considerably reduce the dynamic range of the recorded
              diffraction patterns. Images can be reconstructed reliably and
              robustly from this setup, even when scatter from the specimen is
              weak. A series of ptychographic reconstructions at X-ray energies
              around the L absorption edge of iron demonstrates the advantages
              of this method for soft X-ray spectromicroscopy, which can
              readily provide chemical sensitivity without the need for optical
              refocusing. In particular, the phase signal is in perfect
              registration with the modulus signal and provides complementary
              information that can be more sensitive to changes in the local
              chemical environment.",
  journal  = "Nat. Commun.",
  volume   =  4,
  pages    = "1669",
  year     =  2013,
  language = "en"
}


@ARTICLE{Maiden2011-up,
  title    = "Superresolution imaging via ptychography",
  author   = "Maiden, Andrew M and Humphry, Martin J and Zhang, Fucai and
              Rodenburg, John M",
  abstract = "Coherent diffractive imaging of objects is made considerably more
              practicable by using ptychography, where a set of diffraction
              patterns replaces a single measurement and introduces a high
              degree of redundancy into the recorded data. Here we demonstrate
              that this redundancy allows diffraction patterns to be
              extrapolated beyond the aperture of the recording device, leading
              to superresolved images, improving the limit on the finest
              feature separation by more than a factor of 3.",
  journal  = "J. Opt. Soc. Am. A Opt. Image Sci. Vis.",
  volume   =  28,
  number   =  4,
  pages    = "604--612",
  month    =  apr,
  year     =  2011,
  language = "en"
}



@ARTICLE{Vellekoop2007-ke,
  title    = "Focusing coherent light through opaque strongly scattering media",
  author   = "Vellekoop, I M and Mosk, A P",
  abstract = "We report focusing of coherent light through opaque scattering
              materials by control of the incident wavefront. The multiply
              scattered light forms a focus with a brightness that is up to a
              factor of 1000 higher than the brightness of the normal diffuse
              transmission.",
  journal  = "Opt. Lett.",
  volume   =  32,
  number   =  16,
  pages    = "2309--2311",
  month    =  aug,
  year     =  2007,
  language = "en"
}


@ARTICLE{Anand2010-ji,
  title    = "Three-dimensional microscopy with single-beam wavefront sensing
              and reconstruction from speckle fields",
  author   = "Anand, Arun and Javidi, Bahram",
  journal  = "Opt. Lett.",
  volume   =  35,
  number   =  5,
  pages    = "766--768",
  month    =  mar,
  year     =  2010,
  language = "en"
}



@ARTICLE{Eglese1990-dv,
  title    = "Simulated annealing: A tool for operational research",
  author   = "Eglese, R W",
  abstract = "This paper describes the Simulated Annealing algorithm and the
              physical analogy on which it is based. Some significant
              theoretical results are presented before describing how the
              algorithm may be implemented and some of the choices facing the
              user of this method. An overview is given of the experience of
              experiments with SA and some suggestions are made for ways to
              improve the performance of the algorithm by modifying the `pure'
              SA approach.",
  journal  = "Eur. J. Oper. Res.",
  volume   =  46,
  number   =  3,
  pages    = "271--281",
  month    =  jun,
  year     =  1990,
  keywords = "Simulated annealing; heuristics; optimisation"
}


@ARTICLE{Maiden2017-iz,
  title     = "Further improvements to the ptychographical iterative engine",
  author    = "Maiden, Andrew and Johnson, Daniel and Li, Peng",
  abstract  = "Ptychography is a form of phase imaging that uses iterative
               algorithms to reconstruct an image of a specimen from a series
               of diffraction patterns. It is swiftly developing into a
               mainstream technique, with a growing list of applications across
               a range of imaging modalities. As the field has advanced,
               numerous reconstruction algorithms have been proposed, yet the
               early approaches have not seen major improvement and remain
               popular. In this paper, we revisit the first such algorithm, the
               ptychographical iterative engine (PIE), and show how a simple
               revision and powerful extension can deliver an order of
               magnitude speed increase and handle difficult data sets where
               the original version fails completely.",
  journal   = "Optica, OPTICA",
  publisher = "Optical Society of America",
  volume    =  4,
  number    =  7,
  pages     = "736--745",
  month     =  jul,
  year      =  2017,
  keywords  = "Image processing;Partial coherence;Phase
               imaging;Ptychography;Reconstruction algorithms;Three dimensional
               imaging;",
  language  = "en"
}



@ARTICLE{Maiden2012-bj,
  title    = "An annealing algorithm to correct positioning errors in
              ptychography",
  author   = "Maiden, A M and Humphry, M J and Sarahan, M C and Kraus, B and
              Rodenburg, J M",
  abstract = "Ptychography offers the possibility of improving the resolution
              of atomic-scale (electron and X-ray) transmission microscopy
              without any of the demands of high quality lenses: its resolution
              is in theory only limited by the effective synthetic numerical
              aperture determined by the angular size of the detector. However,
              it has been realised experimentally that a major weakness of the
              approach is that the obtainable resolution is only as good as the
              accuracy to which the illuminating beam can be moved relative to
              the specimen. This can be catastrophic in the electron case
              because of thermal drift and hysteresis in the probe scan coils.
              We present here a computationally efficient extension of the
              'ePIE' ptychographic reconstruction algorithm for correcting
              these errors retrospectively. We demonstrate its effectiveness
              using simulations and results from visible light and electron
              beam experiments that show it can correct positioning errors tens
              of times larger than the pixel size in the resulting image.",
  journal  = "Ultramicroscopy",
  volume   =  120,
  pages    = "64--72",
  month    =  sep,
  year     =  2012,
  language = "en"
}


% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Loetgering_undated-wt,
  title    = "Information recovery in ptychographic coherent diffraction
              imaging",
  author   = "Loetgering, Lars and Treffer, David and Rose, Max and
              Vartanyants, Ivan and Wilhein, Thomas",
  abstract = "Ptychographic coherent diffraction imaging has been used to
              overcome the finite space- bandwidth product in classical
              lens-based microscopy and solve the phase problem in lens- less
              imaging. While ptychography harnesses data redundancy to recover
              information about",
  journal  = "dgao-proceedings.de"
}

@ARTICLE{Loetgering2017-re,
  title    = "Data compression strategies for ptychographic diffraction imaging",
  author   = "Loetgering, Lars and Rose, Max and Treffer, David and
              Vartanyants, Ivan A and Rosenhahn, Axel and Wilhein, Thomas",
  abstract = "Ptychography is a computational imaging method for solving
              inverse scattering problems. To date, the high amount of
              redundancy present in ptychographic data sets requires computer
              memory that is orders of magnitude larger than the retrieved
              information. Here, we propose and compare data compression
              strategies that significantly reduce the amount of data required
              for wavefield inversion. Information metrics are used to measure
              the amount of data redundancy present in ptychographic data.
              Experimental results demonstrate the technique to be memory
              efficient and stable in the presence of systematic errors such as
              partial coherence and noise.",
  journal  = "Advanced Optical Technologies",
  volume   =  6,
  number   =  6,
  pages    = "64",
  month    =  dec,
  year     =  2017
}



@ARTICLE{Parrent1966-au,
  title  = "Basic theory of partial coherence",
  author = "Parrent, George B",
  year   =  1966
}


@ARTICLE{Thibault2013-kg,
  title     = "Reconstructing state mixtures from diffraction measurements",
  author    = "Thibault, Pierre and Menzel, Andreas",
  abstract  = "Progress in imaging and metrology depends on exquisite control
               over and comprehensive characterization of wave fields. As
               reflected in its name, coherent diffractive imaging relies on
               high coherence when reconstructing highly resolved images from
               diffraction intensities alone without the need for image-forming
               lenses1,2,3. Fully coherent light can be described adequately by
               a single pure state. Yet partial coherence and imperfect
               detection often need to be accounted for, requiring statistical
               optics or the superposition of states4,5. Furthermore, the
               dynamics of samples are increasingly the very objectives of
               experiments6. Here we provide a general analytic approach to the
               characterization of diffractive imaging systems that can be
               described as low-rank mixed states. We use experimental data and
               simulations to show how the reconstruction technique compensates
               for and characterizes various sources of decoherence
               quantitatively. Based on ptychography7,8, the procedure is
               closely related to quantum state tomography and is equally
               applicable to high-resolution microscopy, wave sensing and
               fluctuation measurements. As a result, some of the most
               stringent experimental conditions in ptychography can be
               relaxed, and susceptibility to imaging artefacts is reduced.
               Furthermore, the method yields high-resolution images of mixed
               states within the sample, which may include quantum mixtures or
               fast stationary stochastic processes such as vibrations,
               switching or steady flows.",
  journal   = "Nature",
  publisher = "Nature Publishing Group, a division of Macmillan Publishers
               Limited. All Rights Reserved.",
  volume    =  494,
  pages     = "68",
  month     =  feb,
  year      =  2013
}



@ARTICLE{Guizar-Sicairos2012-rq,
  title     = "Role of the illumination spatial-frequency spectrum for
               ptychography",
  author    = "Guizar-Sicairos, Manuel and Holler, Mirko and Diaz, Ana and
               Vila-Comamala, Joan and Bunk, Oliver and Menzel, Andreas",
  journal   = "Phys. Rev. B Condens. Matter",
  publisher = "American Physical Society",
  volume    =  86,
  number    =  10,
  pages     = "100103",
  month     =  sep,
  year      =  2012
}


@BOOK{MacKay2004-to,
  title  = "Information Theory, Inference, and Learning Algorithms",
  author = "MacKay, David J C",
  volume =  50,
  pages  = "2544--2545",
  month  =  oct,
  year   =  2004
}



@ARTICLE{Kamilov2015-lp,
  title     = "Learning approach to optical tomography",
  author    = "Kamilov, Ulugbek S and Papadopoulos, Ioannis N and Shoreh,
               Morteza H and Goy, Alexandre and Vonesch, Cedric and Unser,
               Michael and Psaltis, Demetri",
  abstract  = "Optical tomography has been widely investigated for biomedical
               imaging applications. In recent years optical tomography has
               been combined with digital holography and has been employed to
               produce high-quality images of phase objects such as cells. In
               this paper we describe a method for imaging 3D phase objects in
               a tomographic configuration implemented by training an
               artificial neural network to reproduce the complex amplitude of
               the experimentally measured scattered light. The network is
               designed such that the voxel values of the refractive index of
               the 3D object are the variables that are adapted during the
               training process. We demonstrate the method experimentally by
               forming images of the 3D refractive index distribution of Hela
               cells.",
  journal   = "Optica, OPTICA",
  publisher = "Optical Society of America",
  volume    =  2,
  number    =  6,
  pages     = "517--522",
  month     =  jun,
  year      =  2015,
  keywords  = "Image quality;Imaging techniques;Medical imaging;Phase
               retrieval;Three dimensional imaging;Two photon imaging;",
  language  = "en"
}


@ARTICLE{Goldsborough2016-fz,
  title         = "A Tour of {TensorFlow}",
  author        = "Goldsborough, Peter",
  abstract      = "Deep learning is a branch of artificial intelligence
                   employing deep neural network architectures that has
                   significantly advanced the state-of-the-art in computer
                   vision, speech recognition, natural language processing and
                   other domains. In November 2015, Google released
                   $\textit\{TensorFlow\}$, an open source deep learning
                   software library for defining, training and deploying
                   machine learning models. In this paper, we review TensorFlow
                   and put it in context of modern deep learning concepts and
                   software. We discuss its basic computational paradigms and
                   distributed execution model, its programming interface as
                   well as accompanying visualization toolkits. We then compare
                   TensorFlow to alternative libraries such as Theano, Torch or
                   Caffe on a qualitative as well as quantitative basis and
                   finally comment on observed use-cases of TensorFlow in
                   academia and industry.",
  month         =  oct,
  year          =  2016,
  archivePrefix = "arXiv",
  primaryClass  = "cs.LG",
  eprint        = "1610.01178"
}


@ARTICLE{Waller2015-ec,
  title    = "Computational imaging: Machine learning for {3D} microscopy",
  author   = "Waller, Laura and Tian, Lei",
  journal  = "Nature",
  volume   =  523,
  number   =  7561,
  pages    = "416--417",
  month    =  jul,
  year     =  2015,
  language = "en"
}


@ARTICLE{Duarte2019-ve,
  title    = "Computed stereo lensless X-ray imaging",
  author   = "Duarte, J and Cassin, R and Huijts, J and Iwan, B and Fortuna, F
              and Delbecq, L and Chapman, H and Fajardo, M and Kovacev, M and
              Boutu, W and Merdji, H",
  abstract = "Recovering the three-dimensional (3D) properties of artificial or
              biological systems using low X-ray doses is challenging as most
              techniques are based on computing hundreds of two-dimensional
              (2D) projections. The requirement for a low X-ray dose also
              prevents single-shot 3D imaging using ultrafast X-ray sources.
              Here we show that computed stereo vision concepts can be applied
              to X-rays. Stereo vision is important in the field of machine
              vision and robotics. We reconstruct two X-ray stereo views from
              coherent diffraction patterns and compute a nanoscale 3D
              representation of the sample from disparity maps. Similarly to
              brain perception, computed stereo vision algorithms use
              constraints. We demonstrate that phase-contrast images relax the
              disparity constraints, allowing occulted features to be revealed.
              We also show that by using nanoparticles as labels we can extend
              the applicability of the technique to complex samples. Computed
              stereo X-ray imaging will find application at X-ray free-electron
              lasers, synchrotrons and laser-based sources, and in industrial
              and medical 3D diagnosis methods.",
  journal  = "Nat. Photonics",
  month    =  apr,
  year     =  2019
}


@ARTICLE{Song2019-fh,
  title         = "Super-resolution microscopy via ptychographic structured
                   modulation of a diffuser",
  author        = "Song, Pengming and Jiang, Shaowei and Zhang, He and Hoshino,
                   Kazunori and Zhang, Yongbing and Zheng, Guoan",
  abstract      = "We report a new coherent imaging technique, termed
                   ptychographic structured modulation (PSM), for quantitative
                   super-resolution microscopy. In this technique, we place a
                   thin diffuser (i.e., a scattering lens) in between the
                   sample and the objective lens to modulate the complex light
                   waves from the object. The otherwise inaccessible
                   high-resolution object information can thus be encoded into
                   the captured images. We scan the diffuser to different
                   positions and acquire the corresponding images. A
                   ptychographic phase retrieval process is then used to
                   jointly recover the complex object, the unknown diffuser
                   profile, and the defocus coherent transfer function of the
                   system. Unlike the illumination-based super-resolution
                   approach, the recovered image of our approach depends upon
                   how the complex wavefront exits the sample - not enters it.
                   Therefore, the sample thickness becomes irrelevant during
                   reconstruction. After recovery, we can propagate the
                   super-resolution complex wavefront to any plane along the
                   optical axis. We validate our approach using a resolution
                   target, a quantitative phase target, and biological samples.
                   We demonstrate a 4.5-fold resolution gain over the
                   diffraction limit. We also show that a 4-fold resolution
                   gain can be achieved with as few as 30 images. The reported
                   approach may provide a quantitative super-resolution
                   strategy for coherent light, X-ray, and electron imaging.",
  month         =  apr,
  year          =  2019,
  archivePrefix = "arXiv",
  primaryClass  = "eess.IV",
  eprint        = "1904.11832"
}


@ARTICLE{Li2018-lc,
  title     = "Deep speckle correlation: a deep learning approach toward
               scalable imaging through scattering media",
  author    = "Li, Yunzhe and Xue, Yujia and Tian, Lei",
  abstract  = "Imaging through scattering is an important yet challenging
               problem. Tremendous progress has been made by exploiting the
               deterministic input--output ``transmission matrix'' for a fixed
               medium. However, this ``one-to-one'' mapping is highly
               susceptible to speckle decorrelations -- small perturbations to
               the scattering medium lead to model errors and severe
               degradation of the imaging performance. Our goal here is to
               develop a new framework that is highly scalable to both medium
               perturbations and measurement requirement. To do so, we propose
               a statistical ``one-to-all'' deep learning (DL) technique that
               encapsulates a wide range of statistical variations for the
               model to be resilient to speckle decorrelations. Specifically,
               we develop a convolutional neural network (CNN) that is able to
               learn the statistical information contained in the speckle
               intensity patterns captured on a set of diffusers having the
               same macroscopic parameter. We then show for the first time, to
               the best of our knowledge, that the trained CNN is able to
               generalize and make high-quality object predictions through an
               entirely different set of diffusers of the same class. Our work
               paves the way to a highly scalable DL approach for imaging
               through scattering media.",
  journal   = "Optica, OPTICA",
  publisher = "Optical Society of America",
  volume    =  5,
  number    =  10,
  pages     = "1181--1190",
  month     =  oct,
  year      =  2018,
  keywords  = "Absorption coefficient;Complex media;Light scattering;Multiple
               scattering;Scattering media;Speckle patterns;",
  language  = "en"
}


@ARTICLE{Li2018-lc,
  title     = "Deep speckle correlation: a deep learning approach toward
               scalable imaging through scattering media",
  author    = "Li, Yunzhe and Xue, Yujia and Tian, Lei",
  abstract  = "Imaging through scattering is an important yet challenging
               problem. Tremendous progress has been made by exploiting the
               deterministic input--output ``transmission matrix'' for a fixed
               medium. However, this ``one-to-one'' mapping is highly
               susceptible to speckle decorrelations -- small perturbations to
               the scattering medium lead to model errors and severe
               degradation of the imaging performance. Our goal here is to
               develop a new framework that is highly scalable to both medium
               perturbations and measurement requirement. To do so, we propose
               a statistical ``one-to-all'' deep learning (DL) technique that
               encapsulates a wide range of statistical variations for the
               model to be resilient to speckle decorrelations. Specifically,
               we develop a convolutional neural network (CNN) that is able to
               learn the statistical information contained in the speckle
               intensity patterns captured on a set of diffusers having the
               same macroscopic parameter. We then show for the first time, to
               the best of our knowledge, that the trained CNN is able to
               generalize and make high-quality object predictions through an
               entirely different set of diffusers of the same class. Our work
               paves the way to a highly scalable DL approach for imaging
               through scattering media.",
  journal   = "Optica, OPTICA",
  publisher = "Optical Society of America",
  volume    =  5,
  number    =  10,
  pages     = "1181--1190",
  month     =  oct,
  year      =  2018,
  keywords  = "Absorption coefficient;Complex media;Light scattering;Multiple
               scattering;Scattering media;Speckle patterns;",
  language  = "en"
}

@ARTICLE{Kellman2018-uj,
  title         = "Physics-based Learned Design: Optimized {Coded-Illumination}
                   for Quantitative Phase Imaging",
  author        = "Kellman, Michael R and Bostan, Emrah and Repina, Nicole and
                   Waller, Laura",
  abstract      = "Coded-illumination can enable quantitative phase microscopy
                   of transparent samples with minimal hardware requirements.
                   Intensity images are captured with different source patterns
                   and a non-linear phase retrieval optimization reconstructs
                   the image. The non-linear nature of the processing makes
                   optimizing the illumination pattern designs complicated.
                   Traditional techniques for experimental design (e.g.
                   condition number optimization, spectral analysis) consider
                   only linear measurement formation models and linear
                   reconstructions. Deep neural networks (DNNs) can efficiently
                   represent the non-linear process and can be optimized over
                   via training in an end-to-end framework. However, DNNs
                   typically require a large amount of training examples and
                   parameters to properly learn the phase retrieval process,
                   without making use of the known physical models. Here, we
                   aim to use both our knowledge of the physics and the power
                   of machine learning together. We develop a new data-driven
                   approach to optimizing coded-illumination patterns for a LED
                   array microscope for a given phase reconstruction algorithm.
                   Our method incorporates both the physics of the measurement
                   scheme and the non-linearity of the reconstruction algorithm
                   into the design problem. This enables efficient
                   parameterization, which allows us to use only a small number
                   of training examples to learn designs that generalize well
                   in the experimental setting without retraining. We show
                   experimental results for both a well-characterized phase
                   target and mouse fibroblast cells using coded-illumination
                   patterns optimized for a sparsity-based phase reconstruction
                   algorithm. Our learned design results using 2 measurements
                   demonstrate similar accuracy to Fourier Ptychography with 69
                   measurements.",
  month         =  aug,
  year          =  2018,
  archivePrefix = "arXiv",
  primaryClass  = "eess.SP",
  eprint        = "1808.03571"
}


@ARTICLE{Horstmeyer2017-hy,
  title         = "Convolutional neural networks that teach microscopes how to
                   image",
  author        = "Horstmeyer, Roarke and Chen, Richard Y and Kappes, Barbara
                   and Judkewitz, Benjamin",
  abstract      = "Deep learning algorithms offer a powerful means to
                   automatically analyze the content of medical images.
                   However, many biological samples of interest are primarily
                   transparent to visible light and contain features that are
                   difficult to resolve with a standard optical microscope.
                   Here, we use a convolutional neural network (CNN) not only
                   to classify images, but also to optimize the physical layout
                   of the imaging device itself. We increase the classification
                   accuracy of a microscope's recorded images by merging an
                   optical model of image formation into the pipeline of a CNN.
                   The resulting network simultaneously determines an ideal
                   illumination arrangement to highlight important sample
                   features during image acquisition, along with a set of
                   convolutional weights to classify the detected images
                   post-capture. We demonstrate our joint optimization
                   technique with an experimental microscope configuration that
                   automatically identifies malaria-infected cells with 5-10\%
                   higher accuracy than standard and alternative microscope
                   lighting designs.",
  month         =  sep,
  year          =  2017,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CV",
  eprint        = "1709.07223"
}



@ARTICLE{Sinha2017-op,
  title     = "Lensless computational imaging through deep learning",
  author    = "Sinha, Ayan and Lee, Justin and Li, Shuai and Barbastathis,
               George",
  abstract  = "Deep learning has been proven to yield reliably generalizable
               solutions to numerous classification and decision tasks. Here,
               we demonstrate for the first time to our knowledge that deep
               neural networks (DNNs) can be trained to solve end-to-end
               inverse problems in computational imaging. We experimentally
               built and tested a lensless imaging system where a DNN was
               trained to recover phase objects given their propagated
               intensity diffraction patterns.",
  journal   = "Optica, OPTICA",
  publisher = "Optical Society of America",
  volume    =  4,
  number    =  9,
  pages     = "1117--1125",
  month     =  sep,
  year      =  2017,
  keywords  = "Fresnel number;Image processing;Image quality;Neural
               networks;Phase modulation;Phase retrieval;",
  language  = "en"
}


% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@PHDTHESIS{Yilmaz2015-al,
  title    = "Advanced optical imaging with scattering lenses",
  author   = "Y{\i}lmaz, H",
  abstract = "10 Introduction aberrations that are introduced by samples. With
              adaptive optics, aberrations of low order can be compensated [24,
              25]. In a regime that strongly scattering is involved, low order
              aberration compensation approach of adaptive optics is
              insufficient. With the …",
  year     =  2015,
  school   = "Utrecht University"
}


@ARTICLE{Zernike1938-rw,
  title    = "The concept of degree of coherence and its application to optical
              problems",
  author   = "Zernike, F",
  abstract = "Summary The maximum visibility of the interferences obtainable
              from two points in a wave field is defined as their degree of
              coherence $\gamma$. By a simple statistical method general
              formulae are found for deducing $\gamma$ from illumination data.
              For any extended lightsource $\gamma$ is found equal to the
              amplitude in a certain diffraction image. It does not change by
              the use of a condensing lens, but depends only on the aperture of
              the illuminating cone. These properties are applied to the
              microscopic observation of objects in transmitted light.",
  journal  = "Physica",
  volume   =  5,
  number   =  8,
  pages    = "785--795",
  month    =  aug,
  year     =  1938
}


@ARTICLE{Kandel2019-pr,
  title         = "Using Automatic Differentiation as a General Framework for
                   Ptychographic Reconstruction",
  author        = "Kandel, Saugat and Maddali, S and Allain, Marc and
                   Hruszkewycz, Stephan O and Jacobsen, Chris and Nashed,
                   Youssef S G",
  abstract      = "Coherent diffraction imaging methods enable imaging beyond
                   lens-imposed resolution limits. In these methods, the object
                   can be recovered by minimizing an error metric that
                   quantifies the difference between diffraction patterns as
                   observed, and those calculated from a present guess of the
                   object. Efficient minimization methods require analytical
                   calculation of the derivatives of the error metric, which is
                   not always straightforward. This limits our ability to
                   explore variations of basic imaging approaches. In this
                   paper, we propose to substitute analytical derivative
                   expressions with the automatic differentiation method,
                   whereby we can achieve object reconstruction by specifying
                   only the physics-based experimental forward model. We
                   demonstrate the generality of the proposed method through
                   straightforward object reconstruction for a variety of
                   complex ptychographic experimental models.",
  month         =  feb,
  year          =  2019,
  archivePrefix = "arXiv",
  primaryClass  = "eess.IV",
  eprint        = "1902.03920"
}


@ARTICLE{Nashed2017-pd,
  title    = "Distributed Automatic Differentiation for Ptychography",
  author   = "Nashed, Youssef S G and Peterka, Tom and Deng, Junjing and
              Jacobsen, Chris",
  abstract = "Synchrotron radiation light source facilities are leading the way
              to ultrahigh resolution X-ray imaging. High resolution imaging is
              essential to understanding the fundamental structure and
              interaction of materials at the smallest length scale possible.
              Diffraction based methods achieve nanoscale imaging by replacing
              traditional objective lenses by pixelated area detectors and
              computational image reconstruction. Among these methods,
              ptychography is quickly becoming the standard for sub-30
              nanometer imaging of extended samples, but at the expense of
              increasingly high data rates and volumes. This paper presents a
              new distributed algorithm for solving the ptychographic image
              reconstruction problem based on automatic differentiation. Input
              datasets are subdivided between multiple graphics processing
              units (GPUs); each subset of the problem is then solved either
              entirely independent of other subsets (asynchronously) or through
              sharing gradient information with other GPUs (synchronously). The
              algorithm was evaluated on simulated and real data acquired at
              the Advanced Photon Source, scaling up to 192 GPUs. The
              synchronous variant of our method outperformed an existing
              multi-GPU implementation in terms of accuracy while running at a
              comparable execution time.",
  journal  = "Procedia Comput. Sci.",
  volume   =  108,
  pages    = "404--414",
  month    =  jan,
  year     =  2017,
  keywords = "inverse problems; image reconstruction; gradient methods;
              distributed algorithms; X-ray scattering"
}

@INPROCEEDINGS{Ghosh2018-jf,
  title     = "{ADP}: Automatic differentiation ptychography",
  booktitle = "2018 {IEEE} International Conference on Computational
               Photography ({ICCP})",
  author    = "Ghosh, S and Nashed, Y S G and Cossairt, O and Katsaggelos, A",
  abstract  = "Ptychography is an imaging technique which aims to recover the
               complex-valued exit wavefront of an object from a set of its
               diffraction pattern magnitudes. Ptychography is one of the most
               popular techniques for sub-30 nanometer imaging as it does not
               suffer from the limitations of typical lens based imaging
               techniques. The object can be reconstructed from the captured
               diffraction patterns using iterative phase retrieval algorithms.
               Over time many algorithms have been proposed for iterative
               reconstruction of the object based on manually derived update
               rules. In this paper, we adapt automatic differentiation
               framework to solve practical and complex ptychographic phase
               retrieval problems and demonstrate its advantages in terms of
               speed, accuracy, adaptability and generalizability across
               different scanning techniques.",
  publisher = "ieeexplore.ieee.org",
  pages     = "1--10",
  month     =  may,
  year      =  2018,
  keywords  = "image reconstruction;image retrieval;iterative methods;light
               diffraction;ADP;imaging technique;complex-valued exit
               wavefront;diffraction pattern magnitudes;sub-30 nanometer
               imaging;iterative phase retrieval algorithms;iterative
               reconstruction;manually derived update rules;automatic
               differentiation framework;complex ptychographic phase retrieval
               problems;scanning techniques;automatic differentiation
               ptychography;typical lens based imaging techniques;practical
               ptychographic phase retrieval problems;Probes;Diffraction;X-ray
               diffraction;Detectors;Cost function;Imaging;Fourier transforms"
}