Related papers: A simple denoising algorithm using wavelet transfo…

This paper addresses the deconvolution of an image that has been obtained by superimposing many copies of an underlying unknown image of interest. The superposition is assumed to not be exact due to noise, and is described using an error…

The Easy Path Wavelet Transform is an adaptive transform for bivariate functions (in particular natural images) which has been proposed in [1]. It provides a sparse representation by finding a path in the domain of the function leveraging…

Image denoising is getting more significance, especially in Computed Tomography (CT), which is an important and most common modality in medical imaging. This is mainly due to that the effectiveness of clinical diagnosis using CT image lies…

In spite of the huge literature on deconvolution problems, very little is done for hybrid contexts where signals are quantized. In this paper we undertake an information theoretic approach to the deconvolution problem of a simple integrator…

This paper has been withdrawn by the author due to a crucial error in several equations.

This paper has been withdrawn by the authors due to an incorrect analysis.

Image denoising is a classical problem in low level computer vision. Model-based optimization methods and deep learning approaches have been the two main strategies for solving the problem. Model-based optimization methods are flexible for…

This paper has been withdrawn by the author due to a crucial sign error in equation 1

This paper has been withdrawn.

This paper has been withdrawn by the author due a few mistakes in the paper.

This paper has been withdrawn by the authors, due a oversimplified decoherence model. It will be substituted by a new work.

This paper has been withdrawn by the author.

The notion of wavelets is defined. It is briefly described {\it what} are wavelets, {\it how} to use them, {\it when} we do need them, {\it why} they are preferred and {\it where} they have been applied. Then one proceeds to the…

This submission has been withdrawn because it is a duplicate of [math.PR/0609434].

In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot…

This paper has been withdrawn.

This paper has been withdrawn by the author and replaced by arXiv:0809.4751

This paper has been withdrawn by the author

Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set, where the inputs are…

This paper has been withdrawn by the author due to similarity to the author's other paper