title: Spatially adaptive block-based super-resolution.
abstract: Super-resolution technology provides an effective way to increase image resolution by incorporating additional information from successive input images or training samples. Various super-resolution algorithms have been proposed based on different assumptions, and their relative performances can differ in regions of different characteristics within a single image. Based on this observation, an adaptive algorithm is proposed in this paper to integrate a higher level image classification task and a lower level super-resolution process, in which we incorporate reconstruction-based super-resolution algorithms, single-image enhancement, and image/video classification into a single comprehensive framework. The target high-resolution image plane is divided into adaptive-sized blocks, and different suitable super-resolution algorithms are automatically selected for the blocks. Then, a deblocking process is applied to reduce block edge artifacts. A new benchmark is also utilized to measure the performance of super-resolution algorithms. Experimental results with real-life videos indicate encouraging improvements with our method.
authors: Department of Automation, Tsinghua University, Beijing, China. su-h02@mails.tsinghua.edu.cn
authors: Heng
authors: H
authors: Su
authors: Liang
authors: L
authors: Tang
authors: Ying
authors: Y
authors: Wu
authors: Daniel
authors: D
authors: Tretter
authors: Jie
authors: J
authors: Zhou