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Neural Processing Letters
Erschienen in: Neural Processing Letters 6/2021

15.09.2021

Stack-based Scale-recurrent Network for Face Image Deblurring

verfasst von: Yanqiu Wu, Chaoqun Hong, Xuebai Zhang, Yifan He

Erschienen in: Neural Processing Letters | Ausgabe 6/2021

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Abstract

In recent years, the image deblurring task has attracted more and more researchers’ attention. Many researchers are devoted to eliminating motion blur by using a “coarse-to-fine” architecture. This architecture can effectively eliminate motion blur caused by a simple relative displacement. But when using the architecture directly for the face image deblurring task, there would exist some problems. For example, complex network structures make the model difficult to be trained, and a large number of parameters need to be calculated would result in expensive runtime. Since details of images can’t be completely restored, the quality of images will deteriorate, and deblurring visual effect is poor, so in order to solve these issues, combining the “coarse-to-fine” architecture with the “stacked” architecture, we propose a new network architecture-“Stack-based Scale-recurrent Network” for the face image deblurring task. The ConvLSTM network is employed to build a model. We achieves the purpose of improving the visual clarity and the quality of restored face images. And we use the improved encoder-decoder to enhance network performance. Compared with the other deblurring methods, our method can restore clear face images with higher-quality and clearer vision.
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Metadaten
Titel
Stack-based Scale-recurrent Network for Face Image Deblurring
verfasst von
Yanqiu Wu
Chaoqun Hong
Xuebai Zhang
Yifan He
Publikationsdatum
15.09.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 6/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10604-9

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