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相似性约束的视频超分辨率重建

ISSN号：1006-8961
期刊名称：中国图象图形学报
时间：2013.7.7
页码：761-767
分类：TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] O174.2[理学—数学;理学—基础数学]
作者机构：[1]School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
相关基金：This work was supported by the National Natural Science Foundation of China （61071091, 61071166）, and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institution-Information and Communication Engineering.
相关项目：基于压缩感知的分布式视频编码及其后处理研究

关键词：图像边缘检测, 重建算法, 自适应, 压缩, 感知, 离散小波变换, 纹理图像, 取样, block compressed sensing, edge detection, sampling-adaptive, variance, directional transforms

中文摘要：

在这份报纸，为块适应的采样压缩了基于边察觉(SA-BCS-SPL-ED ) 与光滑的投射 Landweber 察觉到图象重建算法被介绍。这个算法充分利用压缩的块的特征察觉到，它分配取决于它每块的质地复杂性的采样率。块复杂性被它的质地坡度，有高采样率的大变化和小变化的变化与低采样率测量。同时，以便避免在采样上和亚采样，我们为每块建立了最大的采样率和最小的采样率。通过反复的算法，整个图象的实际采样率近似等于到建立价值。在方向性的变换的方面，分离余弦变换(DCT ) ，双树的分离小浪变换(DDWT ) ，分离小浪变换(DWT ) 和 Contourlet (CT ) 在实验被使用。试验性的结果与压缩的块相比显示出那与光滑的投射 Landweber (英国计算机学会系统程序设计语言) 察觉到，建议算法以一样的采样率与简单质地图象和甚至复杂的质地图象是好一些的。而且，当 SA-BCS-SPL-ED-CT 对更多复杂的质地图象仅仅多半更好时， SA-BCS-SPL-ED-DDWT 对图象的大多数相当好。

英文摘要：

In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection （SA-BCS-SPL-ED） image reconstruction algorithm is presented. This algorithm takes full advantage of the characteristics of the block compressed sensing, which assigns a sampling rate depending on its texture complexity of each block. The block complexity is measured by the variance of its texture gradient, big variance with high sampling rates and small variance with low sampling rates. Meanwhile, in order to avoid over-sampling and sub-sampling, we set up the maximum sampling rate and the minimum sampling rate for each block. Through iterative algorithm, the actual sampling rate of the whole image approximately equals to the set up value. In aspects of the directional transforms, discrete cosine transform （DCT）, dual-tree discrete wavelet transform （DDWT）, discrete wavelet transform （DWT） and Contourlet （CT） are used in experiments. Experimental results show that compared to block compressed sensing with smooth projected Landweber （BCS-SPL）, the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate. Besides, SA-BCS-SPL-ED-DDWT is quite good for the most of images while the SA-BCS-SPL-ED-CT is likely better only for more-complicated texture images.

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