Wavelet-Based Total Variation and Nonlocal Similarity Model for Image Denoising
Yan Shen, Qing Liu, Shuqin Lou, Ya-Li Hou · IEEE Signal Processing Letters · 2017
To suppress the heavy noise and keep the distinct edges of the images in the low light condition, we propose a denoising model based on the combination of total variation (TV) and nonlocal similarity in the wavelet domain. The TV regularization in the wavelet domain effectively suppresses the heavy noise with the biorthogonal wavelet function; the nonlocal similarity regularization improves the fine image details. Denoising experiments on artificially degraded and low light images show that in the heavy noise condition, the proposed denoising model can suppress the heavy noise effectively and preserve the detail of images than several state-of-the-art methods.