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Image-denoising algorithm based on improved K-singular value decomposition and atom optimization
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DOI：10.1049/cit2.12044.png)
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The traditional K-singular value decomposition (K-SVD) algorithm has poor image-denoising performance under strong noise. An image-denoising algorithm is proposed based on improved K-SVD and dictionary atom optimization. First, a correlation coefficient-matching criterion is used to obtain a sparser representation of the image dictionary. The dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the dictionary. Then, non-local regularity is incorporated into the denoising model to further improve image-denoising performance. Results of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.
Keyword:
SPARSE-REPRESENTATION
OVERCOMPLETE DICTIONARIES
SVD
期刊
IF:
7.3
论文数:
661
被引数:
2.4K
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