Fast B-spline Transforms for Continuous Image Representation and Interpolation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
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This paper introduces an algorithm to super-resolve an image based on a self-training filter (STF). As in other methods, we first increase the resolution by interpolation. The interpolated image has higher resolution, but is blurry because of the interpolation. Then, unlike other methods, we simply filter this interpolated image to recover some missing high frequency details by STF. The input image is first downsized at the same ratio used in super-resolution, then upsized. The super-resolution filters are obtained by minimizing the mean square error between the upsized image and the input image at different levels of the image pyramid. The best STF is chosen as the one with minimal error in the training phase. We have shown that STF is more effective than a generic unsharp mask filter. By combining interpolation and filtering, we achieved competitive results when compared to support vector regression methods and the kernel regression method.