This paper attempts to improve denoising efficiency of BM3D technique for videos, i.e., VBM3D. VBM3D
constructs 3D cubes from target video frames by a block matching algorithm that minimizes the residual matching
error. However, such a cube formation results in sacrificing the pixel correlation in the temporal direction. This
paper thus modifies this step to preserve the sub-pixel alignment, which makes the Fourier coefficients of each
cube located on a vicinity of a certain plane in the 3-D Fourier domain. Then, SURE-shrinkage technique is
separately applied to the inside and outside of the vicinity of the plane to denoise each cube. The experimental
results given in this paper demonstrate the validity of our approach.
Total-variation-based techniques for JPEG artifact removal have been proposed in the literature, which first formulate the artifact removal as a constrained minimization problem, and then solve it based on a convex optimization method. However, the techniques often require large computational cost due to a large number of iteration steps. In this paper, we thus propose a fast solution technique based on another convex optimization method, referred to as the accelerated alternating direction method of multipliers. The experimental results demonstrate that the proposed solution technique is advantageous over the conventional ones in terms of computational cost.
Total-variation-minimization-based JPEG decompression is recognized as a promising technique for providing artifact-free images. Such a technique is typically accomplished in solving an optimization problem based on iterative methods. However, conventional decompression techniques sometimes exhibit over-smoothed regions in decompressed images by increasing iteration count. As a result, users are confused since an optimal iteration number is unknown for an arbitrarily given JPEG bitstream. To overcome the difficulty, we propose a new iterative JPEG decompression technique that combines a total variation minimization strategy with the so-called compressed sensing. In contrast to existing approaches, our technique always provides optimal decompression results, despite increasing iteration count. The experimental results indicate that the proposed technique is advantageous over conventional ones.
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