Histogram-steered image denoising in the Bayesian framework
Mingsong Dou, Chao Zhang, Daojing Wang · 2008
Rather than concentrating on modeling the image prior probability whose structure is defined locally, in this paper we incorporate the global information from a histogram into the Bayesian method for image de-noising. The key insight is that the histogram of an underlying image can be approximately recovered from the image with additive noise by a deconvolution operation. We test our algorithm in an image set commonly used for denoising test, and obtain improved results.