IEEE Trans Image Process - Spatially adaptive block-based super-resolution.

Tópicos

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{ measur(2081) correl(1212) valu(896) }
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Resumo

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.

Resumo Limpo

superresolut technolog provid effect way increas imag resolut incorpor addit inform success input imag train sampl various superresolut algorithm propos base differ assumpt relat perform can differ region differ characterist within singl imag base observ adapt algorithm propos paper integr higher level imag classif task lower level superresolut process incorpor reconstructionbas superresolut algorithm singleimag enhanc imagevideo classif singl comprehens framework target highresolut imag plane divid adaptives block differ suitabl superresolut algorithm automat select block deblock process appli reduc block edg artifact new benchmark also util measur perform superresolut algorithm experiment result reallif video indic encourag improv method

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