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Illustration of super-resolution. The first four images are from the Berkeley segmentation dataset, the fifth is a frame from the Buster Keaton film The Electric House, obtained from The Internet Archive (also used to illustrate denoising), and the remaining are from the supplementary website of Efrat et al (2013), Accurate Blur Models vs. Image Priors in Super-Resolution. This website has results for several other methods applied to these images.

λ is the regularization parameter; higher values give more importance to the prior. The sparse prior refers to the hyper-Laplacian distribution for image gradients with parameter α = 0.8. The AR priors assume that the image gradients are correlated (according to a simple independent 2-D auto-regressive model with correlation parameters 0.3 and 0.6). The blur kernel is either user-supplied (from the Epanechnikov family) with h giving the bandwidth, or estimated from the input image resized using linear interpolation (using a Fourier domain computation which assumes the AR model above). The estimated kernels are cropped to a size of 15 x 15, and entries less than 10% of the maximum are set to 0.

As seen with test images, the Gaussian priors give better visual results for texture, while the Sparse priors perform better for smooth regions. The IID Sparse prior tends to oversmooth.

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