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发表刊物：IEEE International Conference on Image Processing (ICIP)

关键字：Sparse Modelling Restricted Boltzmann Machine Image Super-resolution Dictionary Learning

摘要：Image super-resolution means forming high-resolution images from low-resolution images. In this paper, we develop a new approach based on the deep Restricted Boltzmann Machines (RBM) for image super-resolution. The RBM architecture has ability of learning a set of visual patterns, called dictionary elements from a set of training images. The learned dictionary will be then used to synthesize high resolution images. We test the proposed algorithm on both benchmark and natural images, comparing with several other techniques. The visual quality of the results has also been assessed by both human evaluation and quantitative measurement.

合写作者：Yi Guo,Ming Yin

第一作者：Junbin Gao

论文类型：会议论文

页面范围：2013, p: 499-503. (CCF C类会议)

是否译文：否

发表时间：2013-09-13