Pattern recognition (psychology)

Motion (physics)

Image (mathematics)

Similarity (geometry)

Image restoration

Artificial intelligence

Set (abstract data type)

Programming language

Deblurring

Code (set theory)

Motion blur

Computer science

Object (grammar)

Computer vision

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