Visual data tensor completion method based on smooth constraint and matrix decomposition

Abstract

The invention discloses a visual data tensor completion method based on smooth constraint and matrix factorization, which comprises the following steps of: firstly, acquiring missing overall data, determining a known data position set omega in the missing overall data, and constructing a corresponding visual data tensor model; then, taking the low-rank tensor completion model as a basic framework, introducing total variation and a tight wavelet framework to carry out smooth constraint, reducing the complexity by utilizing a matrix decomposition technology, and constructing a visual data tensor completion model based on smooth constraint and matrix decomposition; and finally, based on an alternating direction multiplier method, introducing a plurality of auxiliary variables to obtain an augmented Lagrangian function form of the visual data tensor completion model, converting an original optimization problem into a plurality of sub-problems, respectively solving the sub-problems, and outputting a convergence result after multiple iterations, namely a complete visual tensor of completed unknown data. According to the method, more efficient and accurate visual data recovery can be achieved under the condition that large-scale random missing exists in the acquired data.

Description

technical field [0001] The invention belongs to the technical field of signal processing and utilization, in particular to a visual data tensor completion method based on smooth constraints and matrix decomposition. Background technique [0002] With the rapid development of communication technology in modern society, digital visual data has become one of the most important information acquisition and transmission methods in people's daily life and industrial production because it can carry more information and is easier to transmit and store than text content. one. However, in practical applications, visual data is often affected by various factors during the process of generation, transmission, storage, etc., and many important information will be lost. A kind of noise pollution, reducing the visual quality; in the process of data compression transmission, some parts of the visual data may not be reproduced due to signal loss and display holes, etc. Visual data completio...

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Problems solved by technology

At present, traditional tensor completion methods, such as HaLRTC, MFTC, etc., only consider the low rank of tensors, and when the data missing rate is high, the completion accuracy is not high; and the tensor completion method that introduces smooth constraints, Such as SPC, PDS, etc., although it can achieve high-precision data completion with high missing rate, but the calculation complexity is high and the running time is long

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