Gaussian noise estimation with superpixel classification in digital images
Cheng-Ho Wu, Herng-Hua Chang · 2012
Noise estimation is essential in a wide variety of digital image processing applications. It provides an adaptive mechanism for many restoration algorithms instead of using fixed values for the amount of noise. In this paper, we propose a new statistical method based on the superpixel maps for estimating the variance of additive Gaussian noise in images. The proposed approach consists of three major phases: superpixel classification, local variance computation, and statistical determination. Experimental results suggest that the proposed method provides good estimation and is of potential in many image restoration applications that require automation.