Abstract
Low resolution and occlusion are mainly prominent in images taken from certain unconstrained environments such as raw footage from video surveillance. In this work, a deep generative adversarial network for joint face completion and face super-resolution is proposed. It will be really useful in the current COVID-19 scenario as people wearing masks are a common sight. Given an input of a low-resolution face image with occlusion, the generator aims to recover a high-resolution face image without occlusion. The discriminator uses a set of carefully designed losses to assure the high quality of the recovered high-resolution face images without occlusion. Experimental results on CelebA database show that the proposed approach outperforms the state-of-the-art methods in jointly performing face super-resolution and face completion, and shows good generalization ability in cross-database testing. MSSIM showed an accuracy of around 80% for test cases. The recorded values of generator adversarial loss, generator pixel loss, and discriminator loss are 0.93, 0.10, and 0.003, respectively.
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