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In this paper, a novel algorithm is presented for the design of sparse linear-phase FIR filters. Compared to traditional l1-optimization-based methods, the proposed algorithm minimizes l1 norm of a portion (instead of all) of nonzero coefficients. In this way, some nonzero coefficients at crucial positions are not affected by l1 norm utilized in the objective function. The proposed algorithm employs...
In this paper, a group sparse model using Eigenvectors of the Graph Laplacian (EGL) is proposed for image denoising. Unlike the heuristic setting for each image and for each noise deviation in the traditional denoising method via the EGL, in our group-sparse-based method, the used eigenvectors are adaptively selected with the error control. Sequentially, a modified group orthogonal matching pursuit...
Video enhancement has played very important roles in many applications. However, most existing enhancement methods only focus on the spatial quality within a frame while the temporal qualities of the enhanced video are often unguaranteed. In this paper, a new algorithm is proposed for video enhancement. The proposed algorithm introduces new temporal constraints and combines them with the spatial constraints...
Traditional image sharpening methods usually introduce over shootings along the edges, and do not perform well for sharpening an image resulting from high magnification zooming. In this paper, we present an image sharpening algorithm which is overshooting free and particularly suitable for high magnification zooming situation. Our algorithm modifies the pixel value based on the desired changes for...
Genetic algorithms (GAs) are global optimization algorithms which can be used to solve different kinds of problems. However, in the situation that the size of feasible solution space is far less than that of search space, GAs may degrade to random searches. This paper presents an improved genetic algorithm, which adopts variable step-size algorithm to obtain a feasible solution, and reduce search...
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