#7372. Weighted truncated nuclear norm regularization for low-rank quaternion matrix completion

October 2026publication date
Proposal available till 31-05-2025
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Journal’s subject area:
Media Technology;
Electrical and Electronic Engineering;
Signal Processing;
Computer Vision and Pattern Recognition;
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Abstract:
In recent years, quaternion matrix completion (QMC) based on low-rank regularization has been gradually used in image processing. Unlike low-rank matrix completion (LRMC) which handles RGB images by recovering each color channel separately, QMC models retain the connection of three channels and process them as a whole. Most of the existing quaternion-based methods formulate low-rank QMC (LRQMC) as a quaternion nuclear norm (a convex relaxation of the rank) minimization problem. The main limitation of these approaches is that they minimize the singular values simultaneously such that cannot approximate low-rank attributes efficiently.
Keywords:
Low-rank; Quaternion matrix completion; Quaternion truncated nuclear norm; Weights

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