Color image restoration based on nonlocal saturation-value similarity

TL;DR

This paper introduces a novel nonlocal variational approach for color image restoration that leverages saturation-value similarity, leading to improved visual quality and quantitative metrics over existing methods.

Contribution

It develops a new nonlocal regularization method based on saturation-value similarity, enhancing color image restoration beyond traditional patch-based techniques.

Findings

Outperforms existing methods in PSNR, SSIM, QSSIM, and S-CIELAB metrics.

Demonstrates improved visual quality in restored images.

Provides an efficient algorithm with proven convergence.

Abstract

In this paper, we propose and develop a novel nonlocal variational technique based on saturation-value similarity for color image restoration. In traditional nonlocal methods, image patches are extracted from red, green and blue channels of a color image directly, and the color information can not be described finely because the patch similarity is mainly based on the grayscale value of independent channel. The main aim of this paper is to propose and develop a novel nonlocal regularization method by considering the similarity of image patches in saturation-value channel of a color image. In particular, we first establish saturation-value similarity based nonlocal total variation by incorporating saturation-value similarity of color image patches into the proposed nonlocal gradients, which can describe the saturation and value similarity of two adjacent color image patches. The proposed nonlocal variational models are then formulated based on saturation-value similarity based nonlocal total variation. Moreover, we design an effective and efficient algorithm to solve the proposed optimization problem numerically by employing bregmanized operator splitting method, and we also study the convergence of the proposed algorithms. Numerical examples are presented to demonstrate that the performance of the proposed models is better than that of other testing methods in terms of visual quality and some quantitative metrics including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), quaternion structural similarity index (QSSIM) and S-CIELAB color error.