Recovering Texture of Denoised Image via its Statistical Analysis
Yuta Saito, Takamichi Miyata · 2018
This paper proposes a method to recover a texture component which has been lost by weighted nuclear norm minimization - the current state of the art of image denoising. Based on a non-trivial assumption on a statistic between the texture and noise, we can estimate the statistic by using the Stein's lemma. It allows us to recover the texture effectively by using a linear minimum mean squared error estimator (Wiener filter). The experimental results show that our proposed method can improve the image recovery performance of weighted nuclear norm minimization for image denoising (WNNM) in both quantitative and qualitative evaluation.