Improved Image Reconstruction Based on Block Compressed Sensing

Constrained by traditional sampling theory,it's difficult to obtain high resolution image and the sampling data is great. The theory compressed sensing combines the sampling and compressing together under the assumption that the signal is compressible or sparse in a certain sparse transform domain.Compressed sensing needs fewer measurements and it can successfully recover original signal using an optimization process,which will greatly reduce the complexity of sampling and calculation.Since traditional algorithm to sample the whole image is time-consuming and it requires huge storage space,we study block compressed sensing.According to the properties of coefficients, only the high-pass coefficients are measured,then the original image is reconstructed using the orthogonal matching pursuit method.Compared with the original algorithm, simulation result demonstrates that high resolution image can be obtained with the proposed algorithm,which reduces the sampling and storage data.The quality of the reconstruction image is greatly improved.