Unsupervised Class-Specific Deblurring

In this paper, we present an end-to-end deblurring network designed specifically for a class of data. Unlike the prior supervised deep-learning works that extensively rely on large sets of paired data, which is highly demanding and challenging to obtain, we propose an unsupervised training scheme with unpaired data to achieve the same. Our model consists of a Generative Adversarial Network (GAN) that learns a strong prior on the clean image domain using adversarial loss and maps the blurred image to its clean equivalent. To improve the stability of GAN and to preserve the image correspondence, we introduce an additional CNN module that reblurs the generated GAN output to match with the blurred input. Along with these two modules, we also make use of the blurred image itself to self-guide the network to constrain the solution space of generated clean images. This self-guidance is achieved by imposing a scale-space gradient error with an additional gradient module. We train our model on different classes and observe that adding the reblur and gradient modules helps in better convergence. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art supervised methods on both synthetic and real-world images even in the absence of any supervision.