From bf4a2b74f89664e672e588518eaa0661f2b2fb3f Mon Sep 17 00:00:00 2001 From: Ruikang Li <54311152+Lyricccco@users.noreply.github.com> Date: Fri, 27 Sep 2024 21:35:24 +0800 Subject: [PATCH] Add files via upload --- docs/index.html | 463 ++++++++++++++++++++++++++---------------------- 1 file changed, 250 insertions(+), 213 deletions(-) diff --git a/docs/index.html b/docs/index.html index 179b34b..8c2035e 100644 --- a/docs/index.html +++ b/docs/index.html @@ -1,213 +1,250 @@ - - - - - DualDn: Dual-domain Denoising via Differentiable ISP - - - - - - - - - - - - - - - - - - - - - - -
-
-
-
-
-

DualDn: Dual-domain Denoising via Differentiable ISP

-
- - - Ruikang Li1,2, - - Yujin Wang1,†, - - Shiqi Chen2, - - Fan Zhang1, - - Jinwei Gu3, - - Tianfan Xue3, -
- -
- 1Shanghai AI Laboratory, - 2Zhejiang University, - 3The Chinese University of Hong Kong -
†Indicates Corresponding Author -
- -
-
- - - - - - - Paper (coming soon) - - - - - - - - - - - Code (coming soon) - - - - - - - - - - - Data (coming soon) - - - -
-
-
-
-
-
-
- - - -
-
-
-
-
- - -
-

- Camera ISPs Our DualDn
- [Notice that DualDn is only trained on synthetic images, without using any images from these cameras or ISPs during training.] - -

-
-
-
-
- - - -
-
-
-
-

Abstract

-
-
- -
-
-
-

- Image denoising is a critical step in the Image Signal Processor (ISP) of a camera. There are two typical ways to inject a denoiser into the ISP pipeline: a raw domain denoiser that is directly applied to captured raw frames, and an sRGB domain denoiser that is applied to the sRGB image output by the ISP. However, both approaches have their limitations. The residual noise from the raw-domain denoising will be amplified by the ISP pipeline, and the sRGB domain cannot handle spatially varying noise as it only sees noise distorted by ISP processing. As a result, most raw-domain or sRGB-domain denoising works only for specific noise distributions and ISP configurations. -To address these challenges, we propose DualDn, a novel learning-based dual-domain denoising. Unlike previous single-domain denoising, DualDn consists of two denoising networks, one in the raw domain and one in the sRGB domain. The raw domain denoising can adapt to spatially varying noise levels, and the sRGB domain denoising can remove the residual noise amplified by the ISP. Both denoising networks are connected with a differentiable ISP, which is trained end-to-end and discarded during the inference stage. With this design, DualDn achieves greater generalizability compared to most learning-based denoising, as it can adapt to different unseen noises, ISP parameters, and even novel ISP pipelines. -Experiments show that DualDn achieves state-of-the-art performance and can adapt to different denoising network architectures. Moreover, DualDn can be used as a plug-and-play denoising module with real cameras without retraining, and still demonstrate better performance than commercial on-camera denoising, further showing its generalization ability. -

-
-
-
-
-
- - - - -
-
-

BibTeX

- 
@article{li2024dualdn,
-      title={DualDn: Dual-domain Denoising via Differentiable ISP}, 
-      author={Ruikang Li and Yujin Wang and Shiqi Chen and Fan Zhang and Jinwei Gu and Tianfan Xue},
-      journal={arXiv preprint},
-      year={2024}
-}
-
-
- - - - - - - - - - - - - - + + + + + DualDn: Dual-domain Denoising via Differentiable ISP + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
FIRSTFirst none-learning-based denoising was proposed in 2013.
 +
Learning-basedEnd-to-end optimizing.
+
Dual-domainRaw denoiser adapts to noise variations and sRGB denoiser adapts to ISP variations.
+
Denoising Paradigm.Trained with synthetic images, DualDn can successfully generalize to real-captured images with high-quality denoising.
+ +
+
+ +
+
+
+
+
+

DualDn: Dual-domain Denoising via Differentiable ISP

+
+ + + Ruikang Li1,2, + + Yujin Wang1,†, + + Shiqi Chen3, + + Fan Zhang1, + + Jinwei Gu3, + + Tianfan Xue3, +
+ +
+ 1Shanghai AI Laboratory, + 2The Chinese University of Hong Kong, + 3Zhejiang University +
†Indicates Corresponding Author +
+ +
+
+ + + + + + + Paper + + + + + + + + + + + Code (coming within 2 weeks) + + + + + + + + + + + Data (coming within 2 weeks) + + + +
+
+
+
+
+
+
+ + +
+
+ +
+
+
+ + +
+

+ Camera ISPs Our DualDn
+ [Notice that DualDn is only trained on synthetic images, without using any images from these cameras or ISPs during training.] + +

+
+
+
+
+ + + + + +
+
+
+

Existing Problem

+
+
+ +
+
+
+
+
+ + +
+
+
+

Core Design

+
+
+ +
+
+
+
+

+ There are two typical ways to inject a denoiser into the Image Signal Processing (ISP) pipeline: + applying a denoiser directly to captured raw frames (raw domain) or to the ISP's output sRGB images (sRGB domain). +
+ However, both approaches have their limitations. + Residual noise from raw-domain denoising can be amplified by the subsequent ISP processing, and the sRGB domain struggles to handle spatially varying noise since it only sees noise distorted by the ISP. + Consequently, most raw or sRGB domain denoising works only for specific noise distributions and ISP configurations. +
+ Unlike previous single-domain denoising, DualDn consists of two denoising networks: one in the raw domain and one in the sRGB domain. + The raw domain denoising adapts to sensor-specific noise as well as spatially varying noise levels, while the sRGB domain denoising adapts to ISP variations and removes residual noise amplified by the ISP. + Both denoising networks are connected with a differentiable ISP, which is trained end-to-end and discarded during the inference stage. +

+
+
+
+
+
+ + + + +
+
+

BibTeX

+ 
@article{li2024dualdn,
+      title={DualDn: Dual-domain Denoising via Differentiable ISP}, 
+      author={Ruikang Li and Yujin Wang and Shiqi Chen and Fan Zhang and Jinwei Gu and Tianfan Xue},
+      booktitle={European conference on computer vision},
+      year={2024}
+}
+
+
+ + + + + + + + + + + + + + \ No newline at end of file