A Motion Deblurring Disentangled Representation Network

摘要

We present a Motion Deblurring Disentangled Representation Network (MDDRNet), an end-to-end learned method for motion deblurring. There are three main parts in MDDRNet, Blur Loss Function, Disentangled Representation Network (DRN) module, and Structural Convolutional Neural Network (SCNN) module. By converting matched Gram matrix into minimized Maximum Mean Discrepancy (MMD), the Blur Loss Function is obtained for extracting the motion blur features. And by means of novel convolution and pooling layers, the DRN module is designed for motion deblurring. Furthermore, by the SCNN module, the deblurred image is further corrected and restored. Experiment results show that the MDDRNet has best performance compare with five methods, under three kinds of datasets.