A Deep Convolutional Encoder–Decoder–Restorer Architecture for Image Deblurring

The accuracy of many computer vision tasks is reduced by blurred images, so deblur is important. More details of the image can be captured by a common multi-stage network, but the computational complexity of this method is higher compared with a single-stage network. However, a single-stage network...

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Bibliographic Details
Published in:Neural processing letters Vol. 56; no. 1; p. 27
Main Authors: Fan, Yiqing, Hong, Chaoqun, Zeng, Guanghui, Liu, Lijuan
Format: Journal Article
Language:English
Published: New York Springer US 11.02.2024
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Coders
Complex Systems
Computational Intelligence
Computer Science
Computer vision
Datasets
Methods
Neural networks
Deep learning
Encoder–decoder–restorer architecture
Image deblurring
Multi-Scale
ISSN:1573-773X, 1370-4621, 1573-773X
Online Access:Get full text
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Summary:The accuracy of many computer vision tasks is reduced by blurred images, so deblur is important. More details of the image can be captured by a common multi-stage network, but the computational complexity of this method is higher compared with a single-stage network. However, a single-stage network cannot capture multi-scale information well. To tackle the problem, a novel convolutional encoder–decoder–restorer architecture is proposed. In this architecture, a multi-scale input structure is used in the encoder. Improved supervised attention module is inserted into the encoder for enhanced feature acquisition. In decoder, information supplement block is proposed to fuse multi-scale features. Finally, the fused features are used for image recovery in the restorer. In order to optimise the model in multiple domains, the loss function is calculated separately in the spatial and frequency domains. Our method is compared with existing methods on the GOPRO dataset. In addition, to verify the applications of our proposed method, we conduct experiments on the Real image dataset, the VOC2007 dataset and the LFW dataset. Experimental results show that our proposed method outperforms state-of-the-art deblurring methods and improves the accuracy of different vision tasks.
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ISSN:1573-773X
1370-4621
1573-773X
DOI:10.1007/s11063-024-11455-w