基于哈达玛矩阵编码测量的自适应压缩成像

骆乐; 陈钱; 戴慧东; 顾国华; 何伟基

doi:10.3788/YJYXS20183310.0893

您当前的位置：

首页 >

文章列表页 >

基于哈达玛矩阵编码测量的自适应压缩成像

图像处理 | 更新时间：2020-07-06

- 基于哈达玛矩阵编码测量的自适应压缩成像
- Adaptive compressed sampling imaging based on hadamard matrix coding measurement
- 液晶与显示 2018年33卷第10期页码：893-900
- 作者机构：
  
  南京理工大学电子工程与光电技术学院, 江苏南京 210000
- 作者简介：
  
  [ "骆乐(1986-), 男, 江苏宿迁人, 博士研究生, 主要从事光学成像和光电探测等方面的研究。E-mail:wslla@126.com" ]
  [ "何伟基(1981-), 男, 广东韶关人, 博士, 副研究员, 博士生导师, 主要从事光电成像与光电探测等方面的研究。E-mail:hewj@mail.njust.edu.cn" ]
- 基金信息：
  
  武器装备预先研究项目(30102070201);Advance Research Projects of Weapon Equipment(30102070201)
- DOI：10.3788/YJYXS20183310.0893
  中图分类号： TP751
- 收稿日期：2018-08-08，
  
  录用日期：2018-9-10，
  
  纸质出版日期：2018-10-05
- 稿件说明：
移动端阅览
骆乐, 陈钱, 戴慧东, 等. 基于哈达玛矩阵编码测量的自适应压缩成像[J]. 液晶与显示, 2018,33(10):893-900.

Le LUO, Qian CHEN, Hui-dong DAI, et al. Adaptive compressed sampling imaging based on hadamard matrix coding measurement[J]. Chinese journal of liquid crystals and displays, 2018, 33(10): 893-900.
骆乐, 陈钱, 戴慧东, 等. 基于哈达玛矩阵编码测量的自适应压缩成像[J]. 液晶与显示, 2018,33(10):893-900. DOI： 10.3788/YJYXS20183310.0893.

Le LUO, Qian CHEN, Hui-dong DAI, et al. Adaptive compressed sampling imaging based on hadamard matrix coding measurement[J]. Chinese journal of liquid crystals and displays, 2018, 33(10): 893-900. DOI： 10.3788/YJYXS20183310.0893.

摘要

为了消除采样过程中的噪声干扰，进一步提高重构图像质量，针对数字微镜阵列（DMD）与桶探测器在测量过程中点对点采样产生的起伏噪声导致图像信噪比降低的问题，提出基于哈达玛矩阵编码测量的压缩采样成像方法。首先采用DMD分区控制方法，利用哈达玛编码测量，计算获得低分辨率的粗糙图像，接着在预测的重要小波系数所在区域，对同一尺度上的重要区域利用哈达玛矩阵进行投影，同时计算出这些区域的小波系数，最后通过小波逆变换获得重构图像。实验表明，在测量噪声为0.2倍的热噪声下，只需要10%的采样率，通过哈达玛编码测量，图像峰值信噪比从13.98 dB最高提高到34.56 dB，提高了20.58 dB，成像质量明显改善，清晰度高。当存在较大的测量噪声时，该方法可以大幅提高图像的信噪比，尤其适用于微弱光信号条件下的高灵敏压缩采样成像。

Abstract

In order to eliminate the noise interference in the sampling process and to further improve the quality of reconstructed images

a compressed sampling imaging method based on Hadamard matrix encoding measurement is proposed to reduce the signal to noise ratio of the Digital Micro-mirror Device (DMD) and the bucket detector in the measurement process. First

using the DMD zoning control method

the rough image of low resolution is obtained by the Hadamard coding measurement. Then

the important region of the predicted wavelet coefficients is projected with the Hadamard matrix on the important region on the same scale

and the wavelet coefficients of these regions are calculated. Finally

the wavelet inverse transformation is used to get the reconstructed images. The experiment shows that under 0.2 times the thermal noise

only 10% of the sampling rate is needed. The peak signal to noise ratio(PSNR) of the image is increased from 13.98 dB to 34.56 dB by the Hadamard code

which is improved by 20.58 dB

the image quality is obviously improved and the definition is better. Under weak light signal

when there is a large measurement noise

the method can achieve high SNR with low sampling rate.

关键词

Keywords

references

张杰, 史小平.结合压缩感知和曲波的天文图像去噪[J].光学精密工程, 2017, 25(5):1387-1394.

ZhANG J, SHI X P, Astronomical image denoising with compressed sensing and curvelet[J]. Optics and Precision Engineering, 2017, 25(5):1387-1394. (in Chinese)

闫歌, 许廷发, 马旭, 等.动态测量的高光谱图像压缩感知[J].中国光学, 2018, 11(4):550-559.

YAN G, XU T F, MA X, et al. Hyperspectral image compression sensing based on dynamic measurement[J].Editorial Committee of Chinese Optics, 2018, 11(4):550-559. (in Chinese)

王昕, 吉桐伯, 刘富.结合目标提取和压缩感知的红外与可见光图像融合[J].光学精密工程, 2016, 24(7):1743-1753.

WANG X, JI B T, LIU F. Fusion of infrared and visible images based on target segmentation and compressed sensing[J].Optics and Precision Engineering, 2016, 24(7):1743-1753. (in Chinese)

LI Q F, GUO K Y, TANG B, et al . Scattering center modelling based on compressed sensing principle from under-sampling scattering field data[C]. Geoscience and Remote Sensing Symposium. IEEE , 2016: 2672-2675.

朱明, 高文, 郭立强.压缩感知理论在图像处理领域的应用[J].中国光学, 2011, 4(5):441-447

ZHU M, GAO W, GUO L Q. Application of compressed sensing theory in image processing.[J].Chinese Optics, 2011, 4(5):441-447(in Chinese)

DUARTE M F, Davenport M A, Takhar D, et al. Single-Pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, Special Issue on Compressive Sampling, 2008, 25(2):83-91.

杨鹰, 孔玲君, 刘真.基于压缩感知的多光谱图像去马赛克算法[J].液晶与显示, 2017, 32(1):56-61.

YANG Y, KONG L J, LIU Z. Multi-spectral demosaicking method based on compressive sensing[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(1):56-61.

任越美, 张艳宁, 李映.压缩感知及其图像处理应用研究进展与展望[J].自动化学报, 2014, 40(8):1563-1575.

REN Y M, ZHANG Y N, LI Y. Advances and perspective on compressed sensing and application on image processing[J]. Acta Automatica Sinica, 2014, 40(8):1563-1575.

BERINDE R, INDIK P. Sparse recovery using sparse random matrices[R]. Latin American Conference on Theoretical Informatics, 2010, 6034:157-157.

LU G, DO T T, TRAN T D.Fast compressive imaging using scrambled block Hadamard ensemble[C], European Signal Processing Conference, 2010: 1-5.

DUARTE M, DAVENPORT M, TAKHAR D, et al, Single-pixel imaging via compressive sampling[J], IEEE Signal Process Magazine, 2008, 25(2):83-91.

WAKIN M, LASKA J, DUARTE M, et al . An architecture for compressive imaging[C]. Proceedings of IEEE Conference on Image Processing , 2006, 1273-1276.

STREETER L, BURLINGCLARIDGE G R, CREE M J, et al. Optical full Hadamard matrix multiplexing and noise effects[J]. Applied Optics, 2009, 48(11):2078-2085.

MIZUNO T, IWATA T. Hadamard-transform fluorescence-lifetime imaging[J].Optics Express, 2016, 24(8):8202-8013.

SUN M J, MENG L T, EDGAR M P, et al. A Russian dolls ordering of the Hadamard basis for compressive single-pixel imaging[J]. Scientific Report, 2017, 7(1):3464-3470.

DAI H D, GU G H, HE W J, et al. Adaptive compressed sampling based on extended wavelet trees[J]. Applied Optics, 2014, 53(29):6619-6628.

HARWIT M, SLOANE N J A. Hadamard Transform Optics[M]. New York:Academic Press, 1979.

ZhANG W K, ZHANG Z H, GAO L X. Study of using complementary S matrix to enhance SNR in Hadamard spectrometer[J]. International Journal for Light and Electron Optics, 2014, 125(3):1124-1127.

张毅, 王勇, 岳江, 等. DMD编码哈达玛变换高灵敏成像[J].红外与激光工程, 2015, 44(12):3819-3824.

ZHANG Y, WANG Y, YUE J, et al. High sensitivity imaging based on DMD coding Hadamard Transform[J]. Infrared and Laser Engineering, 2015, 44(12):3819-3824.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据