Search Strategies for Image Multi-distortion Estimation

In this paper, we present a method for estimating the amount of Gaussian noise and Gaussian blur in a distorted image. Our method is based on the MS-SSIM framework which, although designed to measure image quality, is used to estimate the amount of blur and noise in a degraded image given a reference image. Various search strategies such as Newton, Simplex, and brute force search are presented and rigorously compared. Based on quantitative results, we show that the amount of blur and noise in a distorted image can be recovered with an accuracy up to 0.95% and 5.40%, respectively. To our knowledge, such precision has never been achieved before.