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This letter presents an adaptive denoising method based on the singular value decomposition (SVD). By incorporating a global subspace analysis into the scheme of local basis selection, the problems of previous adaptive methods are effectively tackled. Experimental results show that the proposed method achieves outstanding preservation of image details, and(More)
Matching pursuit based on geometric dictionary has shown to be a powerful tool for sparse image representation. The main obstacle to its application in real world is the computational complexity. In this paper, a modified algorithm is presented to address this issue. The dictionary with anisotropic refinement atoms is used to provide the approximation(More)
Myriad filter is widely used in impulsive noise suppressing, however, computing the high order filter polynomial effectively is still a big problem. This paper proposed a fast myriad filter computing method to speed computation. Compared with the classical myriad filter, the proposed method was much easier and simpler. The proposed fast myriad filter, was(More)
This letter introduces a practical algorithm called fast -term pursuit (FMTP) for sparse approximation in redundant dictionaries. As an extension of the popular matching pursuit (MP), FMTP presents a good trade-off between high approximation performance and efficient implementation. Based on the effective estimation of the incoherence among dictionary(More)
The S transform is a time-frequency representation with multi-scale focus. It adopts a scalable Gaussian window function to provide a frequency dependent resolution. However, it still suffers from low resolution, which does not satisfy the high precision seismic imaging. Therefore, we propose the sparse S transform to obtain a sparse and aggregated(More)