Research On The Application Of Improved Block Compressed Sensing Algorithm In Image Reconstruction

As data information exploding of information age,relying on traditional sampling theorem for data sampling shortcomings became apparent,under this background,the sparsity is put forward based on the data compressed sensing CS sampling theorem obtained the rapid development,the theorem combines data sampling and compression together,only essential part coefficient of signal was collected,broke through the Nyquist specific limitations,because of its unique advantages has been widely studied in signal processing,wireless communication,and other fields.On the basis of CS theory,when processing natural two-dimensional images,the traditional method is usually to sample the whole image.The disadvantage is that it requires large dimension observation matrix to observe the image,which is not conducive to calculation and storage.In this case,block sparse signal and image segmentation compressed sensing technology have become a hot topic in current research.Block compressed sensing technology can quickly complete sampling and reconstruction of large-dimensional signals.In this paper,the problem of block image compression perception is studied and improved in depth.The main research work is as follows:Firstly,in this paper,the existing block CS of sparse representation method are studied,analysis the advantages and disadvantages of this kind of algorithm,aiming at this kind of algorithm will lead to block image compression effect of sampling signal reconstruction,basic on the NewSGK algorithm,An improved SGK algorithm is proposed,the improved algorithm basis on NewSGK by Bicriteria optimization least squares algorithm to updated the sparse coefficient again,reduce the error sparse representation of block image signal.The experimental results show that the improved SGK algorithm can achieve better reconstruction success rate,eliminate the block effect problem of reconstructed image signal,reduce image reconstruction error,and improve the quality and visual effect of reconstructed images.Secondly,this paper studies the present observation matrix and reconstruction algorithm of CS sampling theorem,focus on Analysis of the advantages and disadvantages of greed reconstruction algorithm,For this kind of algorithm,the sparsity of block signal needs to be preset,an adaptive sparsity estimation algorithm is proposed,at the same time improved compression sampling matching pursuit algorithm support set selection and eliminate the atom standard,sparsity step size adaptive compression sampling matching pursuit algorithm was proposed,the improved algorithm uses the estimated sparsity as the step size to select and add atoms in the support set,and takes half of the maximum correlation value as the threshold to eliminate the redundant atoms in the support set.Experimental results show that the improved algorithm has better reconstruction success rate,lower image reconstruction error and higher image reconstruction quality and visual effect under the same sparsity standard.