Image noise level estimation based on a new adaptive superpixel classification

Abstract

Accurate estimation of noise level in images plays an important role in different image processing applications. The current algorithms can precisely estimate noise with smooth images, but it is still the challenge to approximate noise level from richly textured images. In this paper, we proposed a new adaptive superpixel classification algorithm for noise estimation in complicated textured images. Firstly, our new superpixel algorithm adapts the finite Gaussian clustering approach, which can better approximate homogeneous patches in noisy images. Then noise information is obtained locally from each superpixel patch. Finally, the best estimation of noise level is calculated with a statistical approach. Experimental results with various kinds of images demonstrate that our method is more accurate and robust compared to the five existing common used algorithms.