A Concatenated Residual Network for Image Deblurring
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Deep convolutional neural network (CNN)-based restoration methods have recently received considerable progress in low level vision tasks, e.g., denoising, supperresolution, inpainting. A plain CNN, however, fails in image deblurring due to the severe pixel superposition caused by blur degradation. In this paper, we propose a novel concatenated residual CNN for image deblurring. Driven by minimum mean square error (MMSE)-based discriminative learning, the solution to image deblurring is interestingly unfolded into a series of iterative residual components, and is analyzed to demonstrate the contribution of iterative residual deconvolution (IRD). Furthermore, IRD motivates us to take one step forward to design CNN for image deblurring. Specifically, a residual CNN unit is employed to substitute the residual iteration, and is then concatenated and finally integrated, resulting in concatenated residual convolutional network (CRCNet). The experimental results demonstrate that CRCNet can not only achieve better quantitative metrics but also recover more visually plausible texture details.
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