Depth-Aided Color Image Inpainting in Quaternion Domain
Shunki Tatsumi, Ryo Hayakawa, Youji Iiguni
TL;DR
The paper addresses color image inpainting in the quaternion domain and demonstrates that depth information can be leveraged by encoding depth as the real part of a quaternion representation. It introduces D-LRQMC, a two-stage method that first applies LRQMC, estimates depth from the tentative result, and then re-applies LRQMC with depth embedded as the real component. Experimental results on BSDS with 30% missing pixels show PSNR/SSIM gains and up to 93% of images benefiting from an appropriate depth pattern, validating the approach. The work highlights depth-depth correlation as a practical enhancement for quaternion-based color image processing and suggests extensions to other quaternion-domain techniques.
Abstract
In this paper, we propose a depth-aided color image inpainting method in the quaternion domain, called depth-aided low-rank quaternion matrix completion (D-LRQMC). In conventional quaternion-based inpainting techniques, the color image is expressed as a quaternion matrix by using the three imaginary parts as the color channels, whereas the real part is set to zero and has no information. Our approach incorporates depth information as the real part of the quaternion representations, leveraging the correlation between color and depth to improve the result of inpainting. In the proposed method, we first restore the observed image with the conventional LRQMC and estimate the depth of the restored result. We then incorporate the estimated depth into the real part of the observed image and perform LRQMC again. Simulation results demonstrate that the proposed D-LRQMC can improve restoration accuracy and visual quality for various images compared to the conventional LRQMC. These results suggest the effectiveness of the depth information for color image processing in quaternion domain.