Unsupervised face image deblurring via disentangled representation learning

Abstract

Unsupervised face image deblurring aims to restore latent clear face images with explicit structure and facial details without relying on labels. Despite the recent progress, most existing approaches ignore the domain shift issue between clear and blur domains, leading to color distortion and loss of detail in deblurred images. To tackle this issue, we propose a symmetrical unsupervised framework including a decoupling stage and a reconstruction stage. In the decoupling stage, we employ disentangled representation and adversarial domain translation to establish discriminative boundaries between content-invariant domains and feature-shift domains. Besides, a clear feature encoder for clear images is introduced to obtain a latent feature space containing more detailed clarity, providing reliable input for subsequent reconstruction. In the reconstruction stage, similar modules are adopted to ensure the integrity of the mutual mapping between clear and blurred images, while domain latent supervision loss is introduced to ensure that the decoupled features and content information from both stages belong to the same domain. Extensive experimental results on popular benchmarks validate the effectiveness of our method by surpassing existing works.