  Removing blur caused by camera shake in images has always been a challenging
problem in computer vision literature due to its ill-posed nature. Motion blur
caused due to the relative motion between the camera and the object in 3D space
induces a spatially varying blurring effect over the entire image. In this
paper, we propose a novel deep filter based on Generative Adversarial Network
(GAN) architecture integrated with global skip connection and dense
architecture in order to tackle this problem. Our model, while bypassing the
process of blur kernel estimation, significantly reduces the test time which is
necessary for practical applications. The experiments on the benchmark datasets
prove the effectiveness of the proposed method which outperforms the
state-of-the-art blind deblurring algorithms both quantitatively and
qualitatively.
