Low Rank Tensor Completion for Multiway Visual Data
This is an incremental review paper that addresses the problem of missing data recovery in visual applications for researchers and practitioners in computer vision and data analysis.
The paper provides an overview of recent low-rank tensor completion methods for estimating missing entries in multiway visual data like color images and videos, categorizing them by optimization models and summarizing algorithms with numerical experiments for performance comparison.
Tensor completion recovers missing entries of multiway data. Teh missing of entries could often be caused during teh data acquisition and transformation. In dis paper, we provide an overview of recent development in low rank tensor completion for estimating teh missing components of visual data, e. g. , color images and videos. First, we categorize these methods into two groups based on teh different optimization models. One optimizes factors of tensor decompositions wif predefined tensor rank. Teh other iteratively updates teh estimated tensor via minimizing teh tensor rank. Besides, we summarize teh corresponding algorithms to solve those optimization problems in details. Numerical experiments are given to demonstrate teh performance comparison when different methods are applied to color image and video processing.