Learning texture image prior for super resolution using Restricted Boltzmann Machine

Abstract

Field of Expert (FoE) [1], which is one of the most popular probabilistic models of natural image prior, has been successfully applied to super resolution. Piecewise smoothness imposed on natural images is, however, a relatively limited model for texture image. In the field of deep learning, various approaches for texture modeling using the Restricted Boltzmann Machine (RBM) achieves or surpasses the state-of-the-art on many tasks such as texture synthesis and inpainting. In this paper, we apply the convolutional RBM (cRBM) to learning a texture prior. The maximum a posteriori (MAP) framework is proposed to utilize the probabilistic texture model well. The experiment is done on the Brodatz Dataset, and our experimental results are shown to be comparable to those using FoE and other super resolution approaches.