@inproceedings{c154ad09217e440bb54ec06f88b7f40b,
title = "Efficient Scale-Recurrent Network Using Generative Adversarial Network for Image Deblurring",
abstract = "Ubiquitous blurry images degrade viewing experiences and performance of video analysis. To be applicable to consumer electronics, the large amount of parameters and high computational load of deblurring network have to be avoided. The existing SRN+ has a small number of parameters (3.9M) and comparable performance. To improve the quality of outputs of a deblurring network (e.g., SRN+) without changing its architecture, this paper proposes the pseudo label based order task for training the discriminator. The proposed funnel soft label further reduces the problem of vanishing gradient during training SRN+ (generator), and the adversarial loss combined with weighted scale-level losses improves quality of deblurring. For GoPro dataset, the proposed scheme outperforms the light version of the state-of-the-art MPRNet in PSNR (+1dB) and number of parameters (70%).",
keywords = "Image deblurring, generative adversarial networks (GAN), loss function, pseudo label, scale recurrent network",
author = "Hsu, {Wei Hsiang} and Tang, {Chih Wei}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics, ICCE 2022 ; Conference date: 07-01-2022 Through 09-01-2022",
year = "2022",
doi = "10.1109/ICCE53296.2022.9730407",
language = "???core.languages.en_GB???",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE International Conference on Consumer Electronics, ICCE 2022",
}