Automatic Estimation and Removal of Noise from a Single Image

Ce Liu, R. Szeliski, Sing Bing Kang, C.L. Zitnick, W.T. Freeman
2008 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches cannot effectively remove color noise produced by today's CCD digital camera. In this paper, we propose a unified framework for two tasks: automatic estimation and removal of color noise from a single image using piecewise smooth image models. We introduce the noise level function (NLF), which is a continuous function describing the noise level as
more » ... a function of image brightness. We then estimate an upper bound of the real NLF by fitting a lower envelope to the standard deviations of per-segment image variances. For denoising, the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the RGB values in each segment. Then, a Gaussian conditional random field (GCRF) is constructed to obtain the underlying clean image from the noisy input. Extensive experiments are conducted to test the proposed algorithm, which is shown to outperform state-of-the-art denoising algorithms. Index Terms-Image denoising, piecewise smooth image model, segmentation-based computer vision algorithms, noise estimation, Gaussian conditional random field, automatic vision system.
doi:10.1109/tpami.2007.1176 pmid:18084060 fatcat:up7bk3qn6jav7ab2r2kin56csq