  In this paper, we present a Fast Motion Deblurring-Conditional Generative
Adversarial Network (FMD-cGAN) that helps in blind motion deblurring of a
single image. FMD-cGAN delivers impressive structural similarity and visual
appearance after deblurring an image. Like other deep neural network
architectures, GANs also suffer from large model size (parameters) and
computations. It is not easy to deploy the model on resource constraint devices
such as mobile and robotics. With the help of MobileNet based architecture that
consists of depthwise separable convolution, we reduce the model size and
inference time, without losing the quality of the images. More specifically, we
reduce the model size by 3-60x compare to the nearest competitor. The resulting
compressed Deblurring cGAN faster than its closest competitors and even
qualitative and quantitative results outperform various recently proposed
state-of-the-art blind motion deblurring models. We can also use our model for
real-time image deblurring tasks. The current experiment on the standard
datasets shows the effectiveness of the proposed method.
