DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art performance both in the structural similarity measure and visual appearance. The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images. The method is 5 times faster than the closest competitor -- DeepDeblur. We also introduce a novel method for generating synthetic motion blurred images from sharp ones, allowing realistic dataset augmentation. The model, code and the dataset are available at https://github.com/KupynOrest/DeblurGANComment: CVPR 2018 camera-read

Effect of image degradation on performance of Convolutional Neural Networks

The use of deep learning approaches in image classification and recognition tasks is growing rapidly and gaining huge importance in research due to the great enhancement they achieve. Particularly, Convolutional Neural Networks (CNN) have shown a great significance in the field of computer vision and image recognition recently. They made an enormous improvement in classification and recognition systems’ accuracy. In this work, an investigation of how image related parameters such as contrast, noise, and occlusion affect the work of CNNs is to be carried out. Also, whether all types of variations cause the same drop to performance and how they rank in that regard is considered. After the experiments were carried out, the results revealed that the extent of effect of each degradation type to be different from others. It was clear that blurring and occlusion affects accuracy more than noise when considering the root mean square error as a common objective measure of the amount of alteration that each degradation caused