Abstract
A photon-limited image can be represented as a pixel matrix limited by the relatively small number of collected photons. The image can also be seen as being contaminated by Poisson noise because the total number of photons follows the Poisson distribution. Through exploitation of the inherent properties of observation combined with application of a denoising method, an image can be significantly restored. In this paper, a hybrid clustering and low-rank regularization-based model (HCLR) is proposed based on the essential features of patch clustering and noise. An efficient Newton-type method is designed to optimize this biconvex problem. Experimental results demonstrate that HCLR achieves competitive denoising performance, especially for high noise levels, compared with state-of-the-art Poisson denoising algorithms.
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