关键词: 图像去噪, 稀疏表示, K-SVD算法, 图像块分类, 过完备字典, 字典优化

Abstract: K-Singular Value Decomposition （K-SVD） algorithm is often used for image denoising by creating an over-complete dictionary for sparse representation. K-SVD algorithm is effective and can keep the original image information as well. However, the image structure is often ignored. Furthermore, noise atoms are still existed in the trained dictionary obtained by K-SVD algorithm, which will result?in the poor denoising performance. According to these limitations, a new denoising algorithm is proposed in this paper. First, a more targeted dictionary is obtained by the classification of noisy image blocks. Second, the dictionary atoms are classified into the noise and noiselesscategories, and then the optimized dictionary will be achieved by replacing the noise atoms by overcomplete?discrete cosine transform dictionary atoms. Third, the image is denoised using the optimized dictionary. Simulation studies show that in comparison with the curvelet-based denoising method, the non-local mean denoising method and the classical K-SVD denoising method, the new approach has better denoising ability.

Key words: image denoising, sparse representation, K-Singular Value Decomposition （K-SVD） algorithm, image blocks classification, overcomplete dictionary, dictionary optimization