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资源说明：Spatially Adaptive Block-Based Super-ResolutionSuper-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.
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