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面向异构图像压缩感知的阶数自适应多假设重构

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大数据时代到来,使得图像传感应用面临大维度处理与大容量传输的挑战,压缩感知技术及相关算法在一定程度上解决了该问题.然而,现有压缩感知算法存在对异构图像集泛化性不足的问题,需要为此类图像集设计高泛化性的压缩感知重构算法.因此,基于泛化性较高的多假设预测机制,提出一种阶数自适应多假设重构算法.首先通过窗口自适应线性预测器对各块进行预处理,根据预处理获得的相关性指标,改变多假设搜索窗口的大小,并依据相似度对搜索窗口内的预测块进行排序,结合自适应的搜索窗口挑选不同数量的高相似预测块,生成多假设预测的重构图像.选取自然图像集以及X光胸片和脑磁两个异构图像集进行实验,在不同采样率下对比所提算法与传统的多假设压缩感知重构算法以及两种新近提出的基于多假设预测的算法性能.实验结果表明,所提算法具有良好的性能提升.在自然图像集下,相比两种新近提出的基于多假设预测的重构算法,所提算法保持了一定的恢复质量,且运行时间分别减少了 17.5%,28.7%.此外,相比两种新近提出的算法,在胸片图像集下,所提算法分别获得了 1.16dB,1.43dB的平均PSNR提升,以及36.1%,21.5%的平均运行时间减少;在脑磁图像集下,所提算法分别获得了 1.64dB,1.97dB的平均PSNR提升,以及平均28.6%,26.1%的运行时间减少.整体而言,所提算法具有较低的时间复杂度、较高的恢复质量,综合性能更佳.
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

郑颙铣、刘浩、燕帅、陈根龙

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东华大学信息科学与技术学院 上海 201620

压缩感知重构 多假设预测 线性预测器 阶数自适应 异构图像集

国家自然科学基金

62001099

2024

计算机科学
重庆西南信息有限公司(原科技部西南信息中心)

计算机科学

CSTPCD北大核心
影响因子:0.944
ISSN:1002-137X
年,卷(期):2024.51(10)