Super-Resolution Inpainting of Low-resolution Randomly Occluded Face Images
An end-to-end quadruple Super-Resolution Inpainting Generative Adversarial Network(SRIGAN)is proposed in this paper,for low-resolution random occlusion face images.The generative network consists of an encoder,a feature compensation subnetwork,and a decoder constructed with a pyramid attention module.The discriminant network is an improved Patch discriminant network.The network can effectively learn the absent features of the occluded region through a feature compensation subnetwork and a two-stage training strategy.Then,the information is constructed with the decoder with a pyramid attention module and multi-scale reconstruction loss.Hence,the generative network can transform a low-resolution occlusion image into a quadruple high-resolution complete image.Furthermore,the improvements of the loss function and Patch discriminant network are employed to ensure the stability of network training and enhance the performance of the generated network.The effectiveness of the proposed algorithm is verified by comparison and module verification experiments.
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