Search Results for author:
Found 6 papers, 4 papers with code
DCT2net: an interpretable shallow CNN for image denoising
This work tackles the issue of noise removal from images, focusing on the well-known DCT image denoising algorithm.
Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approach
We propose a unified view of unsupervised non-local methods for image denoising that linearily combine noisy image patches.
Unsupervised Linear and Iterative Combinations of Patches for Image Denoising
We introduce a parametric view of non-local two-step denoisers, for which BM3D is a major representative, where quadratic risk minimization is leveraged for unsupervised optimization.
Normalization-Equivariant Neural Networks with Application to Image Denoising
In many information processing systems, it may be desirable to ensure that any change of the input, whether by shifting or scaling, results in a corresponding change in the system response.
A unified framework of non-local parametric methods for image denoising
We propose a unified view of non-local methods for single-image denoising, for which BM3D is the most popular representative, that operate by gathering noisy patches together according to their similarities in order to process them collaboratively.
On normalization-equivariance properties of supervised and unsupervised denoising methods: a survey
Image denoising is probably the oldest and still one of the most active research topic in image processing.
