Selective Image Super-Resolution

In this paper we propose a vision system that performs image Super Resolution (SR) with selectivity. Conventional SR techniques, either by multi-image fusion or example-based construction, have failed to capitalize on the intrinsic structural and semantic context in the image, and performed \blind" resolution recovery to the entire image area. By comparison, we advocate examplebased selective SR whereby selectivity is exemplied in three aspects: region selectivity (SR only at object regions), source selectivity (object SR with trained object dictionaries), and renement selectivity (object boundaries renement using matting). The proposed system takes over-segmented lowresolution images as inputs, assimilates recent learning techniques of sparse coding (SC) and grouped multi-task lasso (GMTL), and leads eventually to a framework for joint gure-ground separation and interest object SR. The eciency of our framework is manifested in our experiments with subsets of the VOC2009 and MSRC datasets. We also demonstrate several interesting vision applications that can build on our system.

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