Quaternion-based bilinear factor matrix norm minimization for color image inpainting

Abstract

As a new color image representation tool, quaternion has achieved excellent results in the color image processing, because it treats the color image as a whole rather than as a separate color space component, thus it can make full use of the high correlation among RGB channels. Recently, low-rank quaternion matrix completion (LRQMC) methods have proven very useful for color image inpainting. In this paper, we propose three novel LRQMC methods based on three quaternion-based bilinear factor (QBF) matrix norm minimization models. Specifically, we define quaternion double Frobenius norm (Q-DFN), quaternion double nuclear norm (Q-DNN) and quaternion Frobenius/nuclear norm (Q-FNN), and then show their relationship with quaternion-based matrix Schatten-p (Q- Schatten-p ) norm for certain p values. The proposed methods can avoid computing quaternion singular value decompositions (QSVD) for large quaternion matrices, and thus can effectively reduce the calculation time compared with existing (LRQMC) methods. The experimental results demonstrate the superior performance of the proposed methods over some state-of-the-art low-rank (quaternion) matrix completion methods.

References in corpus (2)

• Image inpainting: A review
• The Theory of Quaternion Matrix Derivatives

Cited by in corpus (5)

• A general framework for constrained convex quaternion optimization
• Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss
• Quaternion higher-order singular value decomposition and its applications in color image processing
• Weighted Truncated Nuclear Norm Regularization for Low-Rank Quaternion Matrix Completion
• Low Rank Quaternion Matrix Recovery via Logarithmic Approximation