电子学报
電子學報
전자학보
ACTA ELECTRONICA SINICA
2014年
7期
1262-1267
,共6页
张成%程鸿%张芬%沈川%韦穗
張成%程鴻%張芬%瀋川%韋穗
장성%정홍%장분%침천%위수
压缩成像%图像超分辨率%频域振幅编码%4-f 光学架构%单次曝光
壓縮成像%圖像超分辨率%頻域振幅編碼%4-f 光學架構%單次曝光
압축성상%도상초분변솔%빈역진폭편마%4-f 광학가구%단차폭광
compressive imaging%image super-resolution%frequency-domain amplitude encoding%4-f optical architecture%single exposure
超分辨率被认为是光学成像和图像处理的“圣杯”之一。有别于传统的多幅亚像素图像配准融合实现超分辨率的方法面临的配准误差以及高成本问题,得益于大多数图像普遍具有的稀疏表示特性,本文将压缩传感理论引入超分辨率成像,提出一种新的单次曝光频域振幅编码压缩成像方法。利用4-f 傅里叶光学架构实现图像信息的频域0/1振幅随机调制,然后可以使用低分辨率 CCD 器件实现积分下采样记录对应的测量值,最后利用优化方法从少量的测量值中重建原高分辨率图像。模拟实验验证了本文提出的方法可以有效地实现二维图像信息的获取与重构。此外,本文的方法可以有效地处理大尺寸图像的压缩成像问题,具有重要的应用前景。
超分辨率被認為是光學成像和圖像處理的“聖杯”之一。有彆于傳統的多幅亞像素圖像配準融閤實現超分辨率的方法麵臨的配準誤差以及高成本問題,得益于大多數圖像普遍具有的稀疏錶示特性,本文將壓縮傳感理論引入超分辨率成像,提齣一種新的單次曝光頻域振幅編碼壓縮成像方法。利用4-f 傅裏葉光學架構實現圖像信息的頻域0/1振幅隨機調製,然後可以使用低分辨率 CCD 器件實現積分下採樣記錄對應的測量值,最後利用優化方法從少量的測量值中重建原高分辨率圖像。模擬實驗驗證瞭本文提齣的方法可以有效地實現二維圖像信息的穫取與重構。此外,本文的方法可以有效地處理大呎吋圖像的壓縮成像問題,具有重要的應用前景。
초분변솔피인위시광학성상화도상처리적“골배”지일。유별우전통적다폭아상소도상배준융합실현초분변솔적방법면림적배준오차이급고성본문제,득익우대다수도상보편구유적희소표시특성,본문장압축전감이론인입초분변솔성상,제출일충신적단차폭광빈역진폭편마압축성상방법。이용4-f 부리협광학가구실현도상신식적빈역0/1진폭수궤조제,연후가이사용저분변솔 CCD 기건실현적분하채양기록대응적측량치,최후이용우화방법종소량적측량치중중건원고분변솔도상。모의실험험증료본문제출적방법가이유효지실현이유도상신식적획취여중구。차외,본문적방법가이유효지처리대척촌도상적압축성상문제,구유중요적응용전경。
Super resolution (SR) is being considered as one of the “holy grails” of optical imaging and image processing . Different from the registration error and costly problem faced in multiple subpixel image registration fusion method to achieve super -resolution ,this paper introduces the compressive sensing theory into super-resolution imaging ,which benefit from the general sparse representation of most nature images ,and proposes a novel single-exposure frequency-domain amplitude encoding compressive imaging method .Exploiting the 4-f Fourier optics architecture for modulating the image information by the 0/1 amplitude randomly in the frequency domain ,low-resolution CCD device can then be used to records the corresponding measured values by integral downsampling and finally apply optimization methods to reconstruct the original high-resolution images from small number of mea-sured values .Simulation experiments demonstrate that the 2D image information can be effectively acquired and reconstruction from the measured data by our proposed method .In addition ,our method can effectively deal with large-scale image compressive imaging problem and thus has an important application prospects .