Image denoising is the first step of any image processing chain. If the digital image were completely noise-free, we would have access to an infinity amount of information. Thus, every means to increase the signal to noise ratio must be explored. Early studies applied linear Wiener filters equivalent to a frequency reduction of the Fourier transform. These filters are more efficient when applie...

The utility of magnetic resonance imaging can often be diminished in regions and tissues that suffer from a low signal to noise ratio (SNR). This is especially the case in quantitative MRI where the quantitative parameters being measured can be easily biased by low SNR. In this study, the utility of different denoising algorithms, namely non-local means, bilateral filtering, and block-match and...

Multibaseline (MB) phase unwrapping (PU) is a key processing technique in MB interferometric synthetic aperture radar (InSAR). As one of the most popular methods, cluster analysis (CA)-based MBPU method often suffers from problem low noise robustness. Therefore, block-matching and 3D filtering (BM3D) algorithm, effective methods for image denoising, applied to improve performance method. Five d...

Image denoising by block matching and threedimensionaltransform filtering (BM3D) is a two steps state-ofthe-art algorithm that uses the redundancy of similar blocks innoisy image for removing noise. Similar blocks which can havesome overlap are found by a block matching method and groupedto make 3-D blocks for 3-D transform filtering. In this paper wepropose a new block grouping algorithm in th...

Block-matching and 3D filtering (BM3D) denoising algorithm [1] proposed recently has a problem of computational burden especially for low noise level and a sharp performance drop for high noise level. To solve it, an improved version of BM3D is proposed. The solution combines the digital image characteristic with added noise pollution levels, and adaptively selects block-matching threshold in g...

In addition to the visual information contained in intensity and color, imaging polarimetry allows visual information to be extracted from the polarization of light. However, a major challenge of imaging polarimetry is image degradation due to noise. This paper investigates the mitigation of noise through denoising algorithms and compares existing denoising algorithms with a new method, based o...

Patch based denoising algorithms seek to approximate the conditional expectation of clean patches given their related noisy observations. In this note, we give a probabilistic account of how various algorithms approach this problem and in particular, we argue that small neural networks can denoise small-scale texture patterns almost as well as their large counterparts. The analysis further indi...

image denoising by block matching and threedimensionaltransform filtering (bm3d) is a two steps state-ofthe-art algorithm that uses the redundancy of similar blocks innoisy image for removing noise. similar blocks which can havesome overlap are found by a block matching method and groupedto make 3-d blocks for 3-d transform filtering. in this paper wepropose a new block grouping algorithm in th...

We compare two different formulations of the deblurring problem: one (variational) is de ned by minimization of a single objective function and another one is based on a generalized Nash equilibrium balance of two objective functions. The latter results in the algorithm where the denoising and deblurring operations are decoupled. For image modeling we use the recent BM3D-frames. Simulation expe...