The Research On Image Multi-task Compressive Sensing Based On Wavelet Transform

In recent years,with the rapid development of information technology,people have higher requirements for the quality of images.This creates the immense pressure for signal sampling,transmission and storage.Thus how to relieve the pressure and can effectively extract the useful information from the signals became one of the urgent problems in signal and information processing.Compressed sensing(CS)theory offers a new way to ease the pressure.Compressed sensing(CS)is a theoretical framework that is based on information of sampling.It breaks through the bottleneck of the traditional Nyquist-sampling theorem.It is possible to directly obtain high-resolution signal,and it has obtained the preliminary application in the field of image reconstruction.The research of traditional image compression algorithms are all based on the Nyquist-sampling theorem,which restricted the development of image compression technology.Compressed Sensing is a new emerged hot theory in the field of signal processing in recent years.It breaks through the limitations of Nyqiust theorem.It points out that,as long as the signal is sparse or compressible,CS provides a method for acquiring and compressing data simultaneously,and realizes the change of the sampling methods from signal sampling to information sampling.This paper mainly focuses on multi-tasking image compressed sensing based on wavelet transform.The main work is as follows:(1)We mainly introduces the basic theory of wavelet transform and the wavelet multi-resolution analysis,and summarizes the basic framework of CS theory: signal sparse representation,the design of the observation matrix and the design of signal reconstruction algorithm.And on this basis we introduce the current application field of compressed sensing theory.(2)Two kinds of novel Bayesian reconstruction algorithms for color images' multi-tasking reconstruction process are proposed.We introduce compressed sensing of color images,wavelet transform coefficient characteristics of images,and the learning model of Bayesian compressed sensing(BCS)in detail.Based on hierarchical model,it can pass information among multiple tasks.So Bayesian multi-task learning model can be applied in compressed sensing of color image processing.On this basis,in this paper we propose two kinds of novel Bayesian reconstruction algorithms for color images' multi-tasking reconstruction process.And through the experiments we compare and analyze experimental results,which show that color image compressed sensing based on Bayesian multi-tasking method has the high quality of color image reconstruction.(3)We propuse two novel restructing algorithms for multi-tasking compressed sensing of images.Based on image sparse characteristics after wavelet transform,utilizing the correlations among the high frequency coefficients of three directions,we design algorithms to implement the image reconstruction.In this section,we design two novel restructing algorithms for multi-tasking compressed sensing of images,and compare it with the single task CS reconstruction.Experimental results show that the proposed methods of multi-tasking image compressed sensing have the high quality of image reconstruction.