Abstract
The physical process used in CCD cameras for image formation makes it imperative to simultaneously upsample and deblur the captured images. In this paper, we provide an efficient scheme to solve this problem through a nonlinear end-to-end mapping carried out by a novel deep light-weight residual neural network. The proposed network is designed based on two main modules, namely, image upsampling and image deblurring, aimed for carrying out simultaneously the two tasks involved with the problem. The proposed network employs a residual block with a capacity of generating features in multiple receptive fields and enhancing the network's representational capability. The proposed network is extensively experimented using benchmark datasets for image restoration.
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