Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising

TL;DR

This paper introduces BSWTV, a novel regularization method combining adaptive weighting and eigenvalue-based refinement to improve noisy-image super-resolution and denoising, outperforming existing techniques.

Contribution

The paper proposes BSWTV, a new regularization approach that refines total variation by adaptive weighting and eigenvalue analysis, effectively reducing residual noise and oversmoothing.

Findings

Outperforms state-of-the-art methods on real-world datasets

Achieves superior super-resolution results

Provides promising denoising performance

Abstract

In this paper, we propose a regularization technique for noisy-image super-resolution and image denoising. Total variation (TV) regularization is adopted in many image processing applications to preserve the local smoothness. However, TV prior is prone to oversmoothness, staircasing effect, and contrast losses. Nonlocal TV (NLTV) mitigates the contrast losses by adaptively weighting the smoothness based on the similarity measure of image patches. Although it suppresses the noise effectively in the flat regions, it might leave residual noise surrounding the edges especially when the image is not oversmoothed. To address this problem, we propose the bilateral spectrum weighted total variation (BSWTV). Specially, we apply a locally adaptive shrink coefficient to the image gradients and employ the eigenvalues of the covariance matrix of the weighted image gradients to effectively refine the weighting map and suppress the residual noise. In conjunction with the data fidelity term derived from a mixed Poisson-Gaussian noise model, the objective function is decomposed and solved by the alternating direction method of multipliers (ADMM) algorithm. In order to remove outliers and facilitate the convergence stability, the weighting map is smoothed by a Gaussian filter with an iteratively decreased kernel width and updated in a momentum-based manner in each ADMM iteration. We benchmark our method with the state-of-the-art approaches on the public real-world datasets for super-resolution and image denoising. Experiments show that the proposed method obtains outstanding performance for super-resolution and achieves promising results for denoising on real-world images.