Moving-window varying size 3D transform-based video denoising

In this paper we consider the problem of suppressing additive noise in video data. We propose a transformbased 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 sizes by stacking the matched blocks. An optimal window size is then selected according to the ICI rule and a 3D unitary transform is applied to the selected 3D array. Hard-thresholding on its coefficients attenuates the noise and an inverse 3D transform reconstructs a local estimate of the noise-free signal in the array. The final estimate is a weighted average of the overlapping local ones. Our experiments show that the proposed algorithm outperforms all, known to the authors, video denoising methods, both in terms of objective criteria (L 2 distance) and visual quality. 1

Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space

We propose an effective color image denoising method that exploits ltering 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 each 3D array enables a highly sparse representation of the true signal in a 3D transform domain and thus a subsequent shrinkage of the transform spectra results in effective noise attenuation. The peculiarity of the proposed method is the application of a “grouping constraint ” on the chrominances by reusing exactly the same grouping as for the luminance. The results demonstrate the effectiveness of the proposed grouping constraint and show that the developed denoising algorithm achieves state-of-the-art performance in terms of both peak signal-to-noise ratio and visual quality. Index Terms — color image denoising, adaptive grouping, blockmatching, shrinkage. 1

Image denoising by sparse 3D transform-domain collaborative filtering

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 2-D image fragments (e.g., blocks) into 3-D data arrays which we call “groups.” Collaborative filtering is a special procedure developed to deal with these 3-D groups. We realize it using the three successive steps: 3-D transformation of a group, shrinkage of the transform spectrum, and inverse 3-D transformation. The result is a 3-D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality