光度非均匀彩色序列图像超分辨率重建

贾苏娟; 韩广良; 陈小林; 孙海江

doi:null

您当前的位置：

首页 >

文章列表页 >

光度非均匀彩色序列图像超分辨率重建

成像技术与图像处理 | 更新时间：2020-08-12

- 光度非均匀彩色序列图像超分辨率重建
- Super-resolution reconstruction with photometric change of color image sequence
- 液晶与显示 2014年29卷第1期页码：106-113
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
- 作者简介：
- 基金信息：
  
  国家自然科学基金（No. 61172111）()
- DOI：
  中图分类号： TP911
- 收稿日期：2013-01-11，
  
  修回日期：2013-03-27，
  
  纸质出版日期：2014-02-15
- 稿件说明：
移动端阅览
贾苏娟, 韩广良, 陈小林, 孙海江. 光度非均匀彩色序列图像超分辨率重建[J]. 液晶与显示, 2014,29(1): 106-113

JIA Su-juan, HAN Guang-liang, CHEN Xiao-lin, SUN Hai-jiang. Super-resolution reconstruction with photometric change of color image sequence[J]. , 2014,29(1): 106-113
贾苏娟, 韩广良, 陈小林, 孙海江. 光度非均匀彩色序列图像超分辨率重建[J]. 液晶与显示, 2014,29(1): 106-113 DOI：

JIA Su-juan, HAN Guang-liang, CHEN Xiao-lin, SUN Hai-jiang. Super-resolution reconstruction with photometric change of color image sequence[J]. , 2014,29(1): 106-113 DOI：

摘要

为了实现外界光照变化或相机积分时间自动调节等情况下拍摄的彩色序列图像的超分辨率重建，文章首先提取图像感兴趣区域的方向梯度直方图作为匹配特征，以相关系数作为相似性测度进行几何配准；其次建立光度测量模型进行光度配准；最后采用最大后验估计算法求出光度改善的高分辨率图像。文章采用平均梯度、信息熵、图像对比度对重建图像质量进行评估。实验表明，重建后高分辨率图像平均梯度及信息熵均有大幅提高，图像更清晰、信息更丰富。算法能有效减小重建图像间的光度差异，提高彩色图像的对比度，对光度变化具有一定的鲁棒性。

Abstract

In order to realize the super-resolution reconstruction of color sequence images when the external illumination changes or the camera adjusts integration time automatically

an algorithm has been proposed. Firstly

the images' histograms of oriented gradients (HOG) are extracted as the matching characters and the correlation coefficient is used as the similarity measurements for geometric registration. Secondly

a photometric model is established to achieve photometric registration. Finally

the algorithm of Maximum a Posteriori (MAP) is adopted to get high-resolution images. In order to show the advantage of the algorithm

the results of the average gradient

information entropy and image contrast metric (ICM) of reconstructed images are given. The results show that the average gradient and information entropy of these high-resolution images are enhanced significantly and images have more information. The algorithm is robust to photometric change and it can obviously reduce the photometric difference between the reconstructed images and improve the contrast of color images.

关键词

Keywords

references

Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview[J]. IEEE Signal Processing Magazine, 2003, 20(3): 21-36.[2] Mairal J, Elad M, Sapiro G. Sparse representation for color image restoration[J]. IEEE Transactions on Image Processing, 2008, 17(1):53-69.[3] Gunturk B K, Gevrekci M. High-resolution image reconstruction from multiple differently exposed images[J]. IEEE Signal Process Letters, 2006, 13(4):197-200.[4] Capel D, Zisserman A. Computer vision applied to super resolution[J].IEEE Signal Processing Magazine, 2003, 20(3):75-86.[5] WenYi Zhao, Sarnoff C. Super-resolution with significant illumination change[C]//Proc. Int. Conf. Pattern Recognition, Singapore:Image Processing, 2004:1771-1774.[6] 曹治国, 吴博. 采用HOG特征下的视景象匹配算法[J]. 红外与激光工程, 2012, 41(2):514-516. Cao Z G, Wu B. Approach on scene matching based on histograms of oriented gradients[J]. Infrared and laser engineering, 2012, 41(2):514-516.(in Chinese)[7] Dalal N, Triggs B.Histogram of oriented gradients for human detection[C]//International Conference on Computer Vision and Pattern Recognition, San Diego, USA:Computer Vison and Pattern, 2005, 2: 886-893.[8] Schultz R R, Meng L. Subpixel motion estimation for super-resolution image sequence enhancement[J]. Journal of Visual Communication and Image Representation, 1998, 9(1):38-50.[9] 江静, 张雪松. 图像超分辨率重建算法综述[J]. 红外技术, 2012, 34(1):24-30. Jiang J, Zhang X S. A review of super-resolution reconstruction algorithms[J].Infrared Technology, 2012, 34(1):24-30.(in Chinese)[10] 张剑.图像超分辨率重建问题研究[D].长沙:中南大学, 2010. Zhang J.Research on super resolution reconstruction of images[D]. Changsha: Central South University, 2010.(in Chinese)[11] 邓建青, 刘晶红, 刘铁军. 基于DSP系统的超分辨率图像重建技术研究[J]. 液晶与显示, 2012, 27(1):114-119. Deng J Q, Liu J H, Liu T J. Super-resolution image reconstruction technology based on DSP system[J].Chinese Journal of Liquid Crystals and Displays, 2012, 27(1): 114-119. (in Chinese)[12] Yuan Y H, Zhang J J, Chang B K, et al. Objective quality evaluation of visible and infrared color fusion image[J].Optical Engineering, 2011, 50(3):033202(1-11).

浏览量

137

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

大视场高分辨率无色差的复眼相机阵列系统

基于多尺度残差网络的CT图像超分辨率重建