Color image recovery using low-rank quaternion matrix completion algorithm

TL;DR

This paper introduces a novel low-rank quaternion matrix completion algorithm for color image recovery, combining low-rank approximation techniques and converting quaternion problems into complex matrix problems for improved performance.

Contribution

The paper proposes a new quaternion-based low-rank matrix completion method that integrates two approximation approaches and enhances efficiency by transforming quaternion problems into complex matrix problems.

Findings

Outperforms state-of-the-art tensor-based methods in color image recovery

Demonstrates superior accuracy and efficiency in simulations

Effectively recovers missing data in real-world color images

Abstract

As a new color image representation tool, quaternion has achieved excellent results in color image processing problems. In this paper, we propose a novel low-rank quaternion matrix completion algorithm to recover missing data of color image. Motivated by two kinds of low-rank approximation approaches (low-rank decomposition and nuclear norm minimization) in traditional matrix-based methods, we combine the two approaches in our quaternion matrix-based model. Furthermore, the nuclear norm of the quaternion matrix is replaced by the sum of Frobenius norm of its two low-rank factor quaternion matrices. Based on the relationship between quaternion matrix and its equivalent complex matrix, the problem eventually is converted from quaternion number field to complex number field. An alternating minimization method is applied to solve the model. Simulation results on real world color image recovery show the superior performance and efficiency of the proposed algorithm over some state-of-the-art tensor-based ones.