A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Patch Based Blind Image Super Resolution
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Content based automatic zooming: viewing documents on small displays
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Super resolution using graph-cut
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Hi-index | 0.02 |
This paper presents a novel Resolution-Invariant Image Representation (RIIR) framework, and applies it for Content-Based Zooming (CBZ) applications. We explain how to generate a multi-resolution bases set, from which the learned image representation can be resolution-invariant. This provides the key technology to support the continues image upscaling task for the CBZ applications, which existing example-based resolution enhancement approaches cannot handel, or simply 2-D image interpolation algorithm cannot give satisfactory image quality for. We discuss two clustering based methods to construct the bases set. Experimental results show that, both the two methods give good image quality, and the proposed RIIR framework outperforms existing methods in various aspects.