Order-adaptive Multi-hypothesis Reconstruction for Heterogeneous Image Compressive Sensing

The arrival of the big data era poses challenges for processing and transmitting large amounts of image data.The compressive sensing technology and related algorithms have solved some of these problems to a certain extent.However,existing compressive sensing algorithms still have problems when adapting to heterogeneous image sets.Therefore,it is necessary to design a highly generalized compressive sensing reconstruction algorithm for such image sets.In this paper,an order-adaptive multi-hypothesis reconstruction algorithm is proposed according to a multih-ypothesis prediction mechanism with high generalization.The proposed algorithm preprocesses each block using a window-adaptive linear predictor and changes the size of the multi-hypothesis searching window according to the correlation index obtained from preprocessing.The prediction blocks within the searching window are sorted according to block-wise similarity and different numbers of highly similar prediction blocks are selected from the adaptive searching window for the reconstructed image of multi-hypothesis prediction.Experiments are conducted on a natural image set and two heterogeneous image sets of X-ray chest and brain MRI.At different sampling rates,many experiments and analyses are carried out by comparing the traditional multi-hypothesis compressive sensing reconstruction algorithm and two recent algorithms of multi-hypothesis prediction.The experimental results show a good performance improvement of the proposed algorithm compared to the traditional multihypothesis compressive sensing reconstruction algorithm.On the natural image set,the proposed algorithm maintains a certain recovery quality and achieves an average runtime decrease of 17.5% and 28.7% respectively,compared to two recently proposed algorithms.As compared to two recent proposed algorithms:on the X-ray chest image set,the average PSNR value of proposed algorithm increases by 1.16dB and 1.43dB,and the average runtime decreases by 36.1% and 21.5%,respectively.On the brain MRI image set,the average PSNR value increases by 1.64dB and 1.97dB,and the average runtime decreases by 28.6% and 26.1%,respectively.Overall,the proposed algorithm has low computational complexity and high recovery quality with better tradeoff performance.