With much progress being made, it is recognized that external and internal SR methods each suffer from their certain drawbacks.
However, their complementary properties inspire us to propose the joint SR, that adaptively utilizes both external and internal examples for the SR task. Experimental results demonstrate that joint SR outperforms existing state-of-the-art methods for various test images of different definitions and scaling factors, and is also significantly more favored by user perception.
Super-resolution (SR) algorithms aim to constructing a high-resolution (HR) image from one or multiple low-resolution (LR) input frames. This problem is essentially ill-posed because much information is lost in the HR to LR degradation process. Thus SR has to refer to strong image priors, that range from the simplest analytical smoothness assumptions, to more sophisticated statistical and structural priors learned from natural images.
Popular SR methods rely on example-based learning techniques. Classical example-based methods learn the mapping between LR and HR image patches, from a large and representative external set of image pairs, and is thus denoted as external SR. Meanwhile, images generally possess a great amount of self-similarities; such a self-similarity property motivates a series of internal SR methods.
Zhangyang (Atlas) Wang
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