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Two-Level Wavelet-Based Convolutional Neural Network for Image Deblurring
- Source :
- IEEE Access, Vol 9, Pp 45853-45863 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Image deblurring aims to restore the latent sharp image from the blurred one. In recent years, some learning-based image deblurring methods have achieved significant advances. However, the tradeoff between the texture details and model parameters is still a crucial issue. In this paper, we propose a novel deblurring method based on two-level wavelet-based convolutional neural network (CNN), which embeds discrete wavelet transform (DWT) to separate the image context and texture information and reduces the complexity of calculation. Furthermore, we modify the Inception module by adding pixels-wise attention (PA) mechanism and channel scaling factor to make each convolution kernel have different weights, which increase the receptive field while significantly reduce the parameters of the module. Qualitative and quantitative evaluation on real-word and synthetic datasets shows that the deblurring performance of our method is comparable to the state-of-the-art algorithms. Moreover, compared to the traditional learning-based deblurring method, our model has fewer parameters.
- Subjects :
- Discrete wavelet transform
Deblurring
General Computer Science
Channel (digital image)
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image deblurring
02 engineering and technology
Convolutional neural network
Convolution
Wavelet
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
two-level wavelet-based convolutional neural network (CNN)
Inception module
Image restoration
business.industry
General Engineering
Pattern recognition
Kernel (image processing)
020201 artificial intelligence & image processing
Artificial intelligence
discrete wavelet transform (DWT)
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
pixel-wise attention (PA)
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....1807f48d4550461edae33602d710c693