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A new compressive sensing based image denoising method using block-matching and sparse representations over learned dictionaries

机译：一种新的基于压缩感测的图像去噪方法，使用块匹配和稀疏表示在学习词典中

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摘要

Suppressing noise and preserving detail information such as edges and textures are two key challenges in image denoising. In this paper, a new method for eliminating noise from images is presented which is based on not only compressive sensing but also sparse and redundant representations over trained dictionaries. The objective function of the proposed technique consists of two terms. The first term processes the noisy image by the hard thresholding operator in the bandelet domain to provide the noise-free image as well as guaranteeing the similarity between the denoised image and the noisy image, while the second term ensures that the image admits a sparse decomposition in a dictionary. In addition, the proposed method takes advantage of the block-matching technique for representing the dictionary elements such that the noisy image is firstly grouped by the block-matching technique, and then an identical sparse vector is used for all patches in a group. Simulations using images contaminated by additive white Gaussian noise demonstrate that the performance of the proposed method considerably surpasses that of state-of-the-art methods, both visually and in terms of quantitative criteria, namely peak signal to noise ratio and structural similarity.
机译：抑制噪声和保留细节信息，如边缘和纹理是图像去噪中的两个关键挑战。在本文中，介绍了一种用于消除图像噪声的新方法，其基于不仅是压缩感测，而且还基于训练有素的词典而稀疏和冗余表示。所提出的技术的目标函数由两个术语组成。第一术语通过Bandelet域中的硬阈值操作员处理噪声图像，以提供无噪声图像以及保证去噪图像和嘈杂图像之间的相似性，而第二项可确保图像允许稀疏分解在字典中。另外，所提出的方法利用了用于表示字典元素的块匹配技术，使得噪声图像首先通过块匹配技术分组，然后将相同的稀疏向量用于组中的所有补丁。使用由附加白色高斯噪声污染的图像的模拟表明，所提出的方法的性能显着超越了现有技术，在视觉上以及在定量标准方面，即峰值信号到噪声比和结构相似度。

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