A Local Texture-Constrained Super-Resolution Method

CASIA OpenIR > 智能感知与计算研究中心

	A Local Texture-Constrained Super-Resolution Method
	Qingjie Liu; Yunhong Wang; Zhaoxiang Zhang
	2012-12-04
会议名称	Pacific-Rim Conference on Multimedia
会议录名称	PCM 2012
会议日期	4-6 December 2012
会议地点	Singapore, Singapore
摘要	This paper proposes a local texture constrained super-resolution method for the reconstruction of high-resolution image. Through the learned low/high-resolution patches from training images, the intended high resolution patches are reconstructed using neighbor embedding method. The major contributions of this paper are: 1) Local Binary Pattern (LBP) is adopted to classify the patches into different categories, only those patches who have the same pattern with the input patches are used as candidates; 2) Structural SIMilarity (SSIM) metric which can find the patches with texture most similar to the input is used to search the k most suitable patches in the corresponding category. Experiments show that LBP index can provide proper candidate patches and SSIM metric is better than other metric in finding the most texture similarity patches.
关键词	Image Super-resolution Lbp Neighbor Embedding Ssim
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13256
专题	智能感知与计算研究中心
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Qingjie Liu,Yunhong Wang,Zhaoxiang Zhang. A Local Texture-Constrained Super-Resolution Method[C],2012.