Image Blind Deblurring Algorithm Based on Deep Multi-level Wavelet Transform

Shuzhen CHEN; Shipeng CAO; Meiyue CUI; Qiusheng LIAN

doi:10.11999/JEIT190947

Volume 43 Issue 1

Jan. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(1): 154-161

Shuzhen CHEN, Shipeng CAO, Meiyue CUI, Qiusheng LIAN. Image Blind Deblurring Algorithm Based on Deep Multi-level Wavelet Transform[J]. Journal of Electronics & Information Technology, 2021, 43(1): 154-161. doi: 10.11999/JEIT190947

Citation:

Shuzhen CHEN, Shipeng CAO, Meiyue CUI, Qiusheng LIAN. Image Blind Deblurring Algorithm Based on Deep Multi-level Wavelet Transform[J]. Journal of Electronics & Information Technology, 2021, 43(1): 154-161. doi: 10.11999/JEIT190947

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Image Blind Deblurring Algorithm Based on Deep Multi-level Wavelet Transform

doi: 10.11999/JEIT190947

1.
Institute of Information Science and Technology, Yanshan University, Qinhuangdao 066004, China
2.
Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066004, China

Funds: The National Natural Science Foundation of China (61471313), The Natural Science Foundation of Hebei Province (F2019203318)

Received Date: 2019-11-27
Rev Recd Date: 2020-10-29

Available Online: 2020-11-25

Publish Date: 2021-01-15

Abstract

Abstract

In recent years, convolutional neural networks are widely used in single image deblurring problems. The receptive field size and network depth of convolutional neural networks can affect the performance of image deblurring algorithms. In order to improve the performance of image deblurring algorithm by increasing the receptive field, an image blind deblurring algorithm based on deep multi-level wavelet transform is proposed. Embedding the wavelet transform into the encoder-decoder architecture enhances the sparsity of the image features while increasing the receptive field. In order to reconstruct high-quality images in the wavelet domain, the paper leverges to multi-scale dilated dense block to extract multi-scale information of images, and introduces feature fusion blocks to fuse adaptively features between encoder and decoder. In addition, due to the difference in representation of image information between the wavelet domain and the spatial domain, in order to fuse these different feature representations, the spatial domain reconstruction module is used to improve further the quality of the reconstructed image in the spatial domain. The experimental results show that the proposed method has better performance on Structural SIMilarity index (SSIM) and Peak Signal-to-Noise Ratio, and has better visual effects on real blurred images.
- Image deblurring,
- Deep learning,
- Wavelet transform,
- Multi-scale,
- Feature fusion

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References(20)

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