Deep Convolutional Neural Network for Image Deconvolution Deep Convolutional Neural Network for Image Deconvolution
Paper summary This paper presents a method for nonblind deconvolution of blurry images, that also can also fix artifacts (e.g. compression, clipping) in the input, and is robust to deviations from the input generation model. A convolutional network is used both to deblur and fix artifacts; deblurring is performed using a sequence of horizontal and vertical conv kernels, taking advantage of a high degree of separability in the pseudoinverse blur kernel, and are initialized with a decomposition of the pseudoinverse. A standard compact-kernel convnet is stacked on top, allowing further fixing of artifacts and noise, and traned end-to-end with pairs of blurry and ground truth images.
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Deep Convolutional Neural Network for Image Deconvolution
Xu, Li and Ren, Jimmy S. J. and Liu, Ce and Jia, Jiaya
Neural Information Processing Systems Conference - 2014 via Bibsonomy
Keywords: dblp


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