Adaptive compressed sensing-based non-local reconstruction method for natural image

Abstract

The invention discloses an adaptive compressed sensing-based non-local reconstruction method for a natural image. The problems of serious reconstructed image information loss and the like in the prior art are mainly solved. The method is implemented by the steps of: (1) dividing an image into N 32*32 sub-blocks, obtaining a basic sensing matrix Phi' according to a basic sampling rate b and a sensing matrix Phi, and sampling a signal by utilizing Phi' to obtain a basic observation vector ybi; (2) estimating a standard deviation sequence {d1, d2, ..., and dN} of the image according to the basic observation vector ybi; (3) adaptively allocating a sampling rate ai for each sub-block according to the standard deviation sequence {d1, d2, ..., and dN}, and constructing an adaptive sensing matrix Phi<ai>, and sampling the signal by utilizing the adaptive sensing matrix Phi<ai> to obtain an adaptive observation vector ybi; (4) forming an observation vector yi of each sub-block by using the basic observation vector and the adaptive observation vector; (5) obtaining an initial solution x0 of the image according to the observation vector y; and (6) performing iteration by using x0, and reconstructing the original image until consistency with a finishing condition is achieved to obtain a reconstructed image x'. The method has the advantages of high image reconstruction quality, clear principle and operational simplicity, and is applied to the sampling and reconstruction of the natural image.

Description

technical field [0001] The invention belongs to the field of digital image processing, in particular to an adaptive sampling strategy and a non-local reconstruction method for storing, transmitting and processing natural images. Background technique [0002] Compressed sensing theory is an emerging signal sampling strategy, which successfully integrates the signal sampling process and compression process. The main idea of ​​compressed sensing is: if a signal is sparse or compressible. An observation matrix can be used to project the signal onto a low-dimensional space, where each observation value in the low-dimensional space contains the information of the overall signal, and there is little redundancy between them, so that a small amount of The observed value of the signal is accurately reconstructed by solving a convex programming optimization problem. Obviously, in the theory of compressed sensing, the process of sampling and compressing the signal is carried out at a ...

AI Technical Summary

Problems solved by technology

However, the local filtering method itself has certain defects, such as blurring the edges of the image and incomplete noise removal, which greatly affects the reconstruction effect of the image.

Images