Superpixel-based image noise variance estimation with local statistical assessment

Noise estimation is fundamental and essential in a wide variety of computer vision, image, and video processing applications. It provides an adaptive mechanism for many restoration algorithms instead of using fixed values for the setting of noise levels. This paper proposes a new superpixel-based framework associated with statistical analysis for estimating the variance of additive Gaussian noise in digital images. The proposed approach consists of three major phases: superpixel classification, local variance computation, and statistical determination. The normalized cut algorithm is first adopted to effectively divide the image into a set of superpixel regions, from which the noise variance is computed and estimated. Subsequently, the Jarque-Bera test is used to exclude regions that are not normally distributed. The smallest standard deviation in the remaining regions is finally selected as the estimation result. A wide variety of noisy images with various scenarios were used to evaluate this new noise estimation algorithm. Experimental results indicated that the proposed framework provides accurate estimations across various noise levels. Comparing with many state-of-the-art methods, our algorithm strikes a good compromise between low-level and high-level noise estimations. It is suggested that the proposed method is of potential in many computer vision, image, and video processing applications that require automation.