Image noise refers to random variations in brightness and color information that are not usually part of the original image. We have made a comparative analysis of the algorithms we used so that it performs better in different noise levels.
Image denoising is a well-studied problem in the field of image processing and computer vision. The goal of image denoising is to remove noise from an image while preserving important details and features. There are many different techniques for denoising images, each with its own strengths and weaknesses but we are only focusing on the decomposition methods like:
- Principal Component Analysis (PCA) based denoising is to represent an image as a linear combination of a set of basis images, which are called principal components.
- Singular Value Decomposition (SVD) based denoising is to represent an image as a linear combination of a set of basis images, which are called singular vectors.
- Discrete Cosine Transform (DCT) based denoising is to represent an image as a linear combination of a set of basis images, which are called cosine functions.
- Wavelet Transform based denoising is to decompose the image into different frequency bands using a wavelet transform, and then apply denoising techniques to the different bands.
- Non-negative Matrix Factorization (NMF) based denoising is to represent an image as a linear combination of a set of basis images, which are called non-negative factors.