A deep encoder-decoder networks for joint deblurring and super-resolution

In this paper, we propose an end-to-end convolution neural network (CNN) to restore a clear high-resolution image from a severely blurry image. It's a highly ill-posed problem and brings tremendous challenges to state-of-art deblurring or super-resolution (SR) methods. A straightforward way to solve this problem is to concatenate two types of networks directly. However, experiments show that the concatenation of independent networks increases computation complexity instead of generating satisfying high-resolution images. Consequently, we focus on designing a single deep network to solve the deblurring and SR problems in parallel. Our method, called ED-DSRN, extends the traditional Super-Resolution network by adding a deblurring branch that shares the same feature maps extracted from an encoder-decoder module with the original SR branch. Extensive experiments show that our method produces remarkable deblurred and super-resolved images simultaneously with high efficiency.