Although the recent advances in the sparse representations of images have achieved outstanding denosing results, removing real, structured noise in digital videos remains a challenging problem. We show the utility of reliable motion estimation to establish temporal correspondence across frames in order to achieve high-quality video denoising. In this paper, we propose an adaptive video denosing...

Noise Suppression from images is one of the most important concerns in digital image processing. Impulsive noise is one such noise, which may corrupt images during their acquisition or transmission or storage etc. Removing noise from any processed image is very important noise should be removed in such a way that important information of image should be preserved. For removing salt and pepper n...

in the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. to the best of our knowledge, the effect of noise factors on feature extraction has not been c...

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...