A New Cross-Space Total Variation Regularization Model for Color Image Restoration with Quaternion Blur Operator
This addresses color artifact reduction in image deblurring for computer vision applications, representing a novel method for a known bottleneck.
The authors tackled the cross-channel deblurring problem in color images by proposing a novel cross-space total variation regularization model with a quaternion blur operator, which achieved higher quality restorations than state-of-the-art methods in terms of PSNR, SSIM, MSE, and CIEde2000 metrics.
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.