Abstract
Compressed sensing theory has great advantages on data acquisition, data storage and transmission, data analysis and processing. It has become a research hotspot in recent years. Considering the difference of information distribution in most image signals, at the encoding side, image entropy estimation and edge detection mechanism were used to calculate information content on the base of classification of image blocks. Then we sampled the image in two different angles: image blocks were classified according to the information content into smooth, transitional, and texture blocks, different measurement rates were used; According to the distribution characteristics of the information content, we use different sampling rate allocation strategies for sampling. At the decoding side, different linear operators were constructed to do linear reconstruction according to the different types of image blocks and further using improved iterative thresholding algorithm to remove block effect and noise. The experiments proved that the algorithm improved the quality of reconstructed image and reduced the reconstruction time at the same time and is also available for images with many textures and edges.
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