Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels

Abstract: We propose a simple method for estimating noise level from a single color image. In most image-denoising algorithms, an accurate noise-level estimate results in good denoising performance; however, it is difficult to estimate noise level from a single image because it is an ill-posed problem. We tackle this problem by using prior knowledge that textures are highly correlated between RGB channels and noise is uncorrelated to other signals. We also extended our method for RAW images because they are available in almost all digital cameras and often used in practical situations. Experiments show the high noise-estimation performance of our method in synthetic noisy images. We also applied our method to natural images including RAW images and achieved better noise-estimation performance than conventional methods.