Octave Residual Network for Image Motion Deblurring

Abstract

Image motion deblurring technology is an essential topic. Traditional methods with a high algorithm complexity when estimating the blur kernel require a lot of calculations. To solve this problem, we proposed Octave convolution residual block, and proposed Octave Residual Network (ORN) based on this block. ORN is a Generative Adversarial Networks, where the generator is a multi-scale recurrent network, and the discriminator is a deep neural network. ORN restore sharp images in an end-to-end manner where blur is caused by various sources. We present multi-scale loss function that mimics conventional coarse-to-fine approaches. To verify the validity of ORN, we trained our model on GOPRO dataset, the peak signal-to-noise ratio (PSNR) of the model reached 30.04, and the operation time of each step is reduced by 0.04 seconds. Experimental results show that our method achieves the state-of-the-art performance of image motion deblurring not only in performance but also in training speed.