Attentive ExFeat based deep generative adversarial network for noise robust face super-resolution

doi:10.1016/j.patrec.2023.03.025

Attentive ExFeat based deep generative adversarial network for noise robust face super-resolution

2023-05-01

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DOI：10.1016/j.patrec.2023.03.025

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A new noise robust face super-resolution model using an attentive ExFeat-based generative adversarial network is proposed in this paper. The proposed model introduces the Exigent Feature Attention Unit (ExFAU) which consists of an Exigent Feature (ExFeat) block with a spatial attention unit to enhance the visual quality of the generated face images. The ExFAU block assists the model in reducing the noise and extracting the micro and high-level facial features. Further, the ExFeat block is followed by a spatial attention unit to focus on specific facial features. This allows us to give more attention to key face at-tributes related features and less to the remaining features. The proposed model repeats the ExFAU block to focus on different facial components and enhance them to improve the overall quality of the resultant face images. Experimental outcomes exhibit that the proposed model gains state-of-the-art performance on the standard datasets, namely CelebAHQ, Helen, and LFW face.(c) 2023 Elsevier B.V. All rights reserved.

Keywords:

Convolutional neural network

Face super-resolution

Face hallucination

Exigent feature attention unit

Exigent feature

Noise robust

Attentive ExFeat based deep generative adversarial network for noise robust face super-resolution

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