Blind deblurring of noisy and blurry images of multi-scale convolutional neural network

The purpose of image blind deconvolution is to estimate the unknown blur kernel from an observed blurred image and recover the original sharp image. Conventional methods used simple models to estimate blur kernel, meaning mistakes were inevitable between estimated blur kernel and the real one. It would cause the final deblurred image unpredictable. A multi-scale convolutional neural network was presented based on the novel residual network. And it restored sharp images in an end-to-end manner without estimating blur kernel. Domain constraint layer was designed to the WGAN, it could restrict parameters initial values and accelerate convergence. A total loss function was designed including perception loss which was based on the multi-scale network and adversarial loss which was based on conditional GAN. Extensive experiments show the superiority of the proposed method over other representative methods in terms of quality and quantity. The method is 4 times faster than the similar methods.