Similarity (geometry)

Motion (physics)

Image restoration

Image (mathematics)

Pattern recognition (psychology)

Artificial intelligence

Set (abstract data type)

Programming language

Code (set theory)

Deblurring

Motion blur

Object (grammar)

Computer vision

Computer science

Image processing

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 - Deep-Deblur [25]. 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/DeblurGAN

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks