Image restoration via multi-scale non-local total variation regularization

Total-variation (TV) regularization is widely adopted in image restoration problems to exploit the local smoothness of image. However, traditional TV regularization only models the sparsity of image gradient at the original scale. This paper introduces a multi-scale TV regularization method which models the image gradient sparsity at different scales, and constrains the gradient magnitude of different scales jointly. As different scales extract different frequency of image, our proposed multi-scale regularization method provides constraints for different frequency components. And for each scale, we adaptively estimate the gradient distribution at a particular pixel from a group of nonlocally searched similar patches. Finally, experimental results demonstrate that the proposed method outperforms the conventional TV regularization methods for image restoration.