A New Cross-Space Total Variation Regularization Model for Color Image Restoration with Quaternion Blur Operator

TL;DR

This paper introduces a novel quaternion-based regularization model for color image deblurring that effectively reduces artifacts and preserves color and spatial information, outperforming existing methods in quality metrics.

Contribution

The paper proposes a new cross-space total variation regularization model using quaternion operators and a splitting method, improving color artifact reduction in deblurring.

Findings

Higher PSNR, SSIM, MSE, and CIEde2000 scores compared to state-of-the-art methods.

Effective preservation of color and spatial details in restored images.

Enhanced reduction of color artifacts through quaternion operator splitting.

Abstract

The cross-channel deblurring problem in color image processing is difficult to solve due to the complex coupling and structural blurring of color pixels. Until now, there are few efficient algorithms that can reduce color artifacts in deblurring process. To solve this challenging problem, we present a novel cross-space total variation (CSTV) regularization model for color image deblurring by introducing a quaternion blur operator and a cross-color space regularization functional. The existence and uniqueness of the solution are proved and a new L-curve method is proposed to find a balance of regularization terms on different color spaces. The Euler-Lagrange equation is derived to show that CSTV has taken into account the coupling of all color channels and the local smoothing within each color channel. A quaternion operator splitting method is firstly proposed to enhance the ability of color artifacts reduction of the CSTV regularization model. This strategy also applies to the well-known color deblurring models. Numerical experiments on color image databases illustrate the efficiency and effectiveness of the new model and algorithms. The color images restored by them successfully maintain the color and spatial information and are of higher quality in terms of PSNR, SSIM, MSE and CIEde2000 than the restorations of the-state-of-the-art methods.