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	NOVEL AUTOREGRESSIVE MODEL BASED ON ADAPTIVE WINDOW-EXTENSION AND PATCH-GEODESIC DISTANCE FOR IMAGE INTERPOLATION
	Yang, Wenhan ; Liu, Jiaying ; Yang, Shuai ; Guo, Zongming
	2015
关键词	Structural variation interpolation autoregressive model pixel similarity
英文摘要	In this paper, we propose a novel autoregressive (AR) model based on the adaptive window and the patch-geodesic distance for the image interpolation. The model combines the information of inner/inter-patch correlation. To model the inner-patch correlation, we introduce a patch-geodesic distance similarity metric. The proposed metric shows the desirable capacity to depict the piecewise-stationarity of natural images. For the inter-patch correlation, we introduce the inter-patch structure variation and propose an adaptive window-extension AR model. The model extends the interpolation window according to the local structural variation, increasing the adaptation without violating the consistency. Comprehensive experiments demonstrate that the proposed method is better than or competitive with state-of-the-art interpolation methods in both objective and subjective quality evaluations.; CPCI-S(ISTP); 1211-1215
语种	英语
出处	40th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/450365]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yang, Wenhan,Liu, Jiaying,Yang, Shuai,et al. NOVEL AUTOREGRESSIVE MODEL BASED ON ADAPTIVE WINDOW-EXTENSION AND PATCH-GEODESIC DISTANCE FOR IMAGE INTERPOLATION. 2015-01-01.

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