稀疏表示理论认为在合适的冗余字典下,图像存在最为稀疏的表示,字典的过完备性,使得通过提取很少量的大系数便能捕获到图像中的重要信息,而且对噪声更加鲁棒.针对图像去噪,为了更好地保留图像特征信息,考虑人眼视觉特性,研究过完备字典对噪声图像特征和边缘信息的有效表示,提出以结构相似为信息保真度的特征保留的稀疏表示去噪算法.实验结果表明,该算法能更好地对图像去噪,对特征和边缘等信息的保留能力更强,得到的图像视觉效果更佳.%According to the theory of sparse representation, images can be sparse-represented by using an appropriately redundant dictionary. The completeness can enable using very few big coefficients to capture the important information of images, and cause more robust to noise. Regarding image de-noising, considering the human visual characteristics, this paper studied the effective representation of characteristics and edge information of noisy image based on complete dictionary. For more effective feature retaining of images, a method of feature-retaining de-noising via sparse representation was proposed, which made the Structural SIMilarity (SSIM) as fidelity measure of the information. The experimental results indicate that the proposed algorithm has a better efficiency of de-noising, enhances the capacity of retaining feature, and gets a better visual effect of de-noised image.
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