An Expected Patch Log Likelihood Denoising Method Based on Internal and External Image Similarity

Expected Patch Log Likelihood with a Sparse Prior

Image priors are of great importance in image restoration tasks. These problems can be addressed by decomposing the degraded image into overlapping patches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In...

Statistical and Adaptive Patch-based Image Denoising

A Novel Image Denoising Method Based on Incoherent Dictionary Learning and Domain Adaptation Technique

In this paper, a new method for image denoising based on incoherent dictionary learning and domain transfer technique is proposed. The idea of using sparse representation concept is one of the most interesting areas for researchers. The goal of sparse coding is to approximately model the input data as a weighted linear combination of a small number of basis vectors. Two characteristics should b...

An Improved Image Denoising Method Based on Wavelet Thresholding

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subban...

An Adaptive Image Denoising Method based on Thresholding

This paper proposes an Adaptive Image Denoising Method based on Thresholding that follows the similar approach as in the NeighShrink method. This method shrinks the noisy wavelet coefficients using an adaptive threshold. The NeighShrink and its versions namely, IAWDMBNC and IIDMWT always produce unfavourable smoothing of edges and details of the noisy image because these methods kill more noisy...