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Image compressed sensing based on the similarity of image blocks

机译：基于图像块相似度的图像压缩感知

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Compressed Sensing (CS) theory has recently received amount of attention in the image compression filed. The sparser the signal has, the better the performance recovery. Most wavelet-based reconstruction methods of CS are developed under the assumption that the small wavelet coefficients are close to zero. In other words, a part of image information has been lost before measurement sampling. As we know, most of images have many similar areas. In order to avoid the image information being lost as little as possible, in this paper, a new CS scheme based on the similarity of image blocks is proposed in wavelet domain. Instead of processing the image as a whole, the image is firstly divided into small image blocks. And a clustering algorithm is presented to gather the similar image blocks into a group. Experiments on images demonstrate favorable performances of the proposed method.
机译：压缩传感（CS）理论最近在提交的图像压缩中获得了注意力。信号具有稀疏性，性能恢复越好。基于小波的基于小波的重建方法是在小小波系数接近零的假设下开发的。换句话说，在测量采样之前，图像信息的一部分已经丢失。正如我们所知，大多数图像都有许多类似的区域。为了避免图像信息尽可能少地丢失，本文提出了一种基于图像块相似性的新CS方案在小波域中。而不是将图像整体处理，而是将图像划分为小图像块。提出了一种聚类算法以将类似的图像块收集到组中。图像实验表明了所提出的方法的有利性表现。

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