Abstract
We propose a deep dictionary model for single image super-resolution (SISR) made of multiple layers of analysis dictionaries interlaced with corresponding soft-thresholding operations and a single synthesis dictionary. In this paper, we introduce a novel method for learning analysis dictionary and thresholding pairs as building block for the deep dictionary model. Each analysis dictionary contains two sub-dictionaries: an information preserving analysis dictionary (IPAD) and a clustering analysis dictionary (CAD). The IPAD and thresholding pair passes the key information from the previous layer, while the CAD and thresholding pair gives a sparse representation of its input data that facilitates discrimination of key features. Simulation results show that the proposed deep dictionary model achieves comparable performance with a deep neural network which has the same structure and is optimized using backpropagation.
Published Version
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