International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Learning-based super-resolution algorithms synthesize high-resolution details by using training data. However, since an input image does not belong to a training image set, there is a limitation in recovering its high-frequency details. In our approach, we build and utilize residual training data to complement missing details. We first estimate a pair of midand high-frequency images of each training image by using ordinary training data. We then build residual training data by obtaining the residual mid- and high-frequency images that denote the difference between the estimation and original. Thereby, we can synthesize high-resolution details better by using both ordinary and residual training data sets. In addition, in order to use training data more efficiently, we adaptively select low-resolution patches in an input image. Experimental results demonstrate that the proposed method can synthesize higher-resolution images compared to the existing algorithms.