Color image restoration based on nonlocal saturation-value similarity

2026-03-19

AI Summary

This work addresses the limitations of traditional nonlocal methods in color image inpainting, which measure patch similarity directly in RGB space and thus fail to accurately capture chromatic structure, thereby compromising restoration performance. To overcome this, the authors propose a novel nonlocal variational model that leverages similarity in the saturation–value (S–V) color space. This is the first approach to integrate S–V similarity into a nonlocal total variation regularization framework, enabling more precise modeling of color relationships among image patches. The method constructs an S–V-driven nonlocal gradient and incorporates it into the total variation regularizer, with an efficient solver based on the Bregmanized operator splitting algorithm. Experimental results demonstrate that the proposed method outperforms state-of-the-art techniques both visually and quantitatively, achieving superior scores in PSNR, SSIM, QSSIM, and S-CIELAB color difference metrics.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• color image restoration
• nonlocal methods
• saturation-value similarity
• patch similarity
• color information

Innovation

• nonlocal total variation
• saturation-value similarity
• color image restoration
• Bregmanized operator splitting
• HSV color space