Efficient image noise estimation based on skewness invariance and adaptive noise injection

The precise estimation of the noise level is a crucial issue in image processing. In this study, the authors propose a new method for noise standard deviation (STD) estimation from natural images based on skewness-scale invariance in the transform domain and an adaptive noise injection strategy. The method is divided into two steps. The first step assumes that the natural clean image has the property of constancy of skewness in the transform domain. Then, a preliminary noise estimation method based on skewness invariance is designed by solving a constrained non-linear optimisation problem. The second step involves noise rectification via noise injection. According to the phenomenon that compared with the high-noise circumstance, the error of preliminary estimation is more serious under a low amount of noise, the noise STD is re-estimated by injecting another noise for which the STD is known. In addition, the threshold model with respect to image complexity is established to identify whether a second estimation is needed. The experimental results demonstrate the efficacy of the proposed method and performance is superior to other state-of-the-art methods.