Research On Block Compressed Sensing Method

Compressed sensing(CS)is a new technique for sampling and compression.It breaks through the Nyquist sampling for acquisition and makes quite a stir in academic and industry circles.On this basis,Lu Gan proposed block compressed sensing,which divided the original image into block and processed each block independently.Block CS is faster in sampling and reconstruction and lower in memory using.In this paper,we mainly do some research in block reconstruction.Under the frame of block CS,we propose compressed sensing based on saliency sub-block and made improvements in the method of image partitioning and reconstruction.The main contents of the paper can be summarized as follows:(1).We proposed block compressed sensing approach based on saliency(SBCS).In this paper,we proposed adaptive block compressive sampling algorithm which is based on saliency image signal,and the tested image was adaptive block preprocessed adaptively.Then the important region was extracted,According to saliency parameters,the measured image was differentiated compression sampling distributed.Thus,it effectively ensure that the image important area was allocated high sampling rate and the background area was allocated less sampling rate,so that it can ensure the efficiency and quality of image reconstruction.Howerver,block compressed sensing always tend to bring blocking effect.To a certain extent,improved block compressed sensing method in this paper can reduce the blocking effect,and further improve the quality of reconstruction.(2).We proposed the method that block compressed sensing method is used in sensor networks(HDABCS).This paper presents a hierarchical network topology,the layered structure combined CS to improve energy efficiency.At the same time,block compressed sensing is an efficient method of compressed sensing.The method of measurement matrix is small,in favor of storage,and can save calculating energy consumption of sensors.It can allocate more sampling rate to important data set,and less sampling rate to unimportant data,so that it can improve the accuracy of the data recovery,reconstruction has a higher accuracy at the same energy consumption.(3).In this paper,a lot of experiments has been done to verify the performance of the proposed algorithm from all angles.Though the experimental comparison,either effect or efficiency of reconstruction and energy consumption,HDABCS show the advantages over existing algorithms.