An Image Variable Sampling and Reconstruction Algorithm Based DWT Multiscale Block Compressed Sensing

JIANG Yewen; YU Xinmei

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An Image Variable Sampling and Reconstruction Algorithm Based DWT Multiscale Block Compressed Sensing

更新时间：2023-12-11

- An Image Variable Sampling and Reconstruction Algorithm Based DWT Multiscale Block Compressed Sensing
- Acta Scientiarum Naturalium Universitatis SunYatseni Vol. 52, Issue 3, Pages: 30-33(2013)
- 作者机构：
  
  佛山科学技术学院电子与信息工程学院,广东,佛山,528000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013，
  
  Published Online：25 May 2013，
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JIANG Yewen, YU Xinmei. An Image Variable Sampling and Reconstruction Algorithm Based DWT Multiscale Block Compressed Sensing. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 52(3):30-33(2013)
DOI：

JIANG Yewen, YU Xinmei. An Image Variable Sampling and Reconstruction Algorithm Based DWT Multiscale Block Compressed Sensing. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 52(3):30-33(2013) DOI：

摘要

利用压缩感知理论改善图像重构的质量是目前图像处理技术研究的焦点。通过DWT域对图像每级分解时的每个子带中应用分块采样并结合平滑投影Landweber重构算法，提出一种多尺度分块变采样率压缩感知图像重构算法。比较BCS-SPL 和 TV 以及多尺度GPSR图像处理算法，文中提出的算法使重构的图像质量提高了１~３dB。

Abstract

At present

utilizing Compressed Sensing(CS) theory to improve image quality of reconstructions is a research focus of image process technology. By deploying blockbased CS variable sampling within the domain of a wavelet transform

a multiscale blockCS algorithm with smoothed projected Landweber reconstruction algorithm is provided. Blockbased CS sampling is applied independently within each subband of each decomposition level of a wavelet transform of a image. Experimental results reveal that the algorithm achieves a 1~3 dB gain in reconstruction PSNR over the BCS-SPL and TV as well as multiscale GPSR algorithm.

关键词

多尺度DWT压缩感知（CS）变采样率重构

Keywords

multiscaleDWTcompressed sensingvariable sampling ratereconstruction

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Related Institution

School of Civil Engineering， Sun Yat-sen University

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中山大学数学系

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