1 research outputs found
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 example-based
selective SR whereby selectivity is exemplified in three aspects: region
selectivity (SR only at object regions), source selectivity (object SR with
trained object dictionaries), and refinement selectivity (object boundaries
refinement using matting). The proposed system takes over-segmented
low-resolution 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 figure-ground separation and interest object SR. The
efficiency 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.Comment: 20 pages, 5 figures. Submitted to Computer Vision and Image
Understanding in March 2010. Keywords: image super resolution, semantic image
segmentation, vision system, vision applicatio