This paper proposes a novel imaging technique which combines clustering sub-dictionary learning and gradient histogram preservation to improve the quality of compressive imaging from two aspects: edge sharpness and noise suppression. Practical experiments further demonstrate better results on practical optical imaging application in terms of weighted peak signal-to-noise ratio and measure of feature similarity index.
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