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 com-pressive 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 de-sign 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-hy-pothesis 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 se-lected 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 experi-ments 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 decrea-ses 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.
Compressive sensing reconstructionMulti-hypothesis predictionLinear predictorOrder-adaptiveHeterogeneous image set