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We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call "groups." Collaborative Altering is a special procedure developed to deal with these 3D groups. We realize it using the(More)
In order to simultaneously sharpen image details and attenuate noise, we propose to combine the recent block-matching and 3D Þltering (BM3D) denoising approach, based on 3D transform-domain collaborative Þltering, with alpha-rooting, a transform-domain sharpening technique. The BM3D exploits grouping of similar image blocks into 3D arrays (groups) on which(More)
We propose an effective video denoising method based on highly sparse signal representation in local 3D transform domain. A noisy video is processed in blockwise manner and for each processed block we form a 3D data array that we call “group” by stacking together blocks found similar to the currently processed one. This grouping is realized as(More)
We propose an image restoration technique exploiting regularized inversion and the recent block-matching and 3D filtering (BM3D) denoising filter. The BM3D employs a non-local modeling of images by collecting similar image patches in 3D arrays. The so-called collaborative filtering applied on such a 3D array is realized by transform-domain shrinkage. In(More)
We propose an effective color image denoising method that exploits filtering in highly sparse local 3D transform domain in each channel of a luminance-chrominance color space. For each image block in each channel, a 3D array is formed by stacking together blocks similar to it, a process that we call "grouping". The high similarity between grouped blocks in(More)
We present a novel approach to still image denoising based on eective filtering in 3D transform domain by combining sliding-window transform processing with block-matching. We process blocks within the image in a sliding manner and utilize the block-matching concept by searching for blocks which are similar to the currently processed one. The matched blocks(More)
— We propose an image denoising method that exploits nonlocal image modeling, principal component analysis (PCA), and local shape-adaptive anisotropic estimation. The nonlocal modeling is exploited by grouping similar image patches in 3-D groups. The denoising is performed by shrinkage of the spectrum of a 3-D transform applied on such groups. The(More)
We present a novel approach to still image denoising based on effective Þltering in 3D transform domain by combining sliding-window transform processing with block-matching. We process blocks within the image in a sliding manner and utilize the block-matching concept by searching for blocks which are similar to the currently processed one. The matched(More)
The shape-adaptive DCT (SA-DCT) can be computed on a support of arbitrary shape, but retains a computational complexity comparable to that of the usual separable block DCT. Despite the near-optimal decorrelation and energy compaction properties, application of the SA-DCT has been rather limited, targeted nearly exclusively to video compression. It has been(More)
In this paper we consider the problem of suppressing additive noise in video data. We propose a transform-based video denoising method in sliding, local 3D variable-sized windows. For every spatial position in each frame we use a block-matching algorithm to collect highly correlated blocks from neighboring frames and form 3D arrays for all predefined window(More)