Super-resolution reconstruction of face images based on iterative upsampling and downsampling layers

Yalu Ren; Rui Li; Yan Liu

doi:10.1117/12.2658883

23 November 2022 Super-resolution reconstruction of face images based on iterative upsampling and downsampling layers

Yalu Ren, Rui Li, Yan Liu

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 124542A (2022) https://doi.org/10.1117/12.2658883
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

In order to solve the current problems of insufficient detail extraction and poor visual effect after high magnification reconstruction of face images, a super-resolution method is proposed for single images of faces based on generative adversarial networks. Channel attention is added to the generative network to extract richer facial details, and the idea of iterative up and down sampling layers in the depth inverse projection network is borrowed to make the reconstructed image with good visual effect after high magnification. For the discriminator network, the normalization layer, which would destroy the image contrast, is removed. The experimental results show that the reconstructed images are more realistic and the visual effects are improved compared with Bicubic, SRCNN, LapSRN and SRGAN.

Citation Download Citation

Yalu Ren, Rui Li, and Yan Liu "Super-resolution reconstruction of face images based on iterative upsampling and downsampling layers", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 124542A (23 November 2022); https://doi.org/10.1117/12.2658883

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KEYWORDS

Visualization

Feature extraction

Reconstruction algorithms

Convolution

Molybdenum

Gallium nitride

Image fusion

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