Non-parametric Bayesian dictionary learning for image super resolution

Abstract

This paper addresses the problem of generating a super-resolution (SR) image from a single low-resolution input image. A non-parametric Bayesian method is implemented to train the over-complete dictionary. The first advantage of using non-parametric Bayesian approach is the number of dictionary atoms and their relative importance may be inferred non-parametrically. In addition, sparsity level of the coefficients may be inferred automatically. Finally, the non-parametric Bayesian approach may learn the dictionary in situ. Two previous state-of-the-art methods including the efficient ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> method and the (K-SVD) are implemented for comparison. Although the efficient ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> method overall produces the best quality super-resolution images, the 837-atom dictionary trained by non-parametric Bayesian method produces super-resolution images that very close to quality of images produced by the 1024-atom efficient ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> dictionary. Finally, the non-parametric Bayesian method has the fastest speed in training the over-complete dictionary.