计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (10): 302-310.doi: 10.11896/jsjkx.230800156

• 计算机图形学&多媒体 • 上一篇    下一篇

面向异构图像压缩感知的阶数自适应多假设重构

郑颙铣, 刘浩, 燕帅, 陈根龙   

  1. 东华大学信息科学与技术学院 上海 201620
  • 收稿日期:2023-08-24 修回日期:2024-01-09 出版日期:2024-10-15 发布日期:2024-10-11
  • 通讯作者: 刘浩(liuhao@dhu.edu.cn)
  • 作者简介:(2222048@mail.dhu.edu.cn)
  • 基金资助:
    国家自然科学基金(62001099)

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

ZHENG Yongxian, LIU Hao, YAN Shuai, CHEN Genlong   

  1. College of Information Science and Technology,Donghua University,Shanghai 201620,China
  • Received:2023-08-24 Revised:2024-01-09 Online:2024-10-15 Published:2024-10-11
  • About author:ZHENG Yongxian,born in 2000,postgraduate,is a member of CCF(No.P8454G).His main research interest is image compression sensing.
    LIU Hao,born in 1977,associate professor,is a member of CCF(No.26679M).His main research interests include multimedia signal processing and intelligent sensing system.
  • Supported by:
    National Natural Science Foundation of China(62001099).

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摘要： 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。

关键词: 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

Abstract: 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.

Key words: Compressive sensing reconstruction, Multi-hypothesis prediction, Linear predictor, Order-adaptive, Heterogeneous image set

中图分类号: 

  • TN919.8
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