Color image completion method based on tensor block cyclic expansion

Abstract

The invention discloses a color image completion method based on tensor block cyclic expansion, and belongs to the technical field of image processing. The method comprises the steps of firstly, inputting a to-be-completed image, carrying out n-nearest neighbor initialization on missing pixels, and obtaining a target image; then initializing model parameters, estimating a block cyclic expansion rank of the target image and setting a weight coefficient; and then inputtin the target image into an image completion model in a tensor form and carrying out convex optimization solving on the model through iteration by adopting an alternating direction multiplier method, wherein the image completion model is a low-rank matrix factorization model based on tensor block cyclic expansion. and finally,carrying out data format conversion on the tensor obtained by iteration, so that the tensor is output in the format of the image to be completed. According to the method, when tensor block cyclic expansion is carried out, the connection between image slices is increased, so that the loss of image structure information caused by expansion operation is reduced to a certain extent; the peak signal-to-noise ratio of the complemented image is significantly improved, and texture and detail information are richer.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a color image complementing method based on tensor block cyclic expansion. Background technique [0002] With the development of the times and the advancement of science and technology, the forms of data representation are constantly being introduced. As far as image data is concerned, it can be represented and processed by matrix in the black and white era; however, in the color era, it becomes difficult to use matrix to process images, so high-order arrays are used to store images to preserve their special structures. Often referred to as tensors. Tensor is a generalization of the concept of vector and matrix, which can be regarded as special first-order tensor and second-order tensor. [0003] Visual information is the most effective information that people perceive and recognize the world in their daily life. From two-dimensional grayscale images to thr...

AI Technical Summary

Problems solved by technology

Tensor decomposition models mainly include CP decomposition, Tucker decomposition, Tensor Train (TT) decomposition, TensorRing (TR) decomposition, and Hierarchical Tensor (HT) decomposition; The original structure of the tensor reduces the connection between tensor elements to a certain extent

As a result, the effect of tensor completion is not satisfactory

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