Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016)
摘要
In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution. We train a FCNN to remove noises in the gradient domain and use the learned gradients to guide the image deconvolution step. In contrast to the existing deep neural network based methods, we iteratively deconvolve the blurred images in a multi-stage framework. The proposed method is able to learn an adaptive image prior, which keeps both local (details) and global (structures) information. Both quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of quality and speed.
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关键词
iterative nonblind deconvolution,image denoising,learned gradients,blurred images,adaptive image,image deconvolution,deep neural network,fully-convolutional network learning,FCNN training,multistage framework,quantitative evaluation,qualitative evaluation
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