A Dual-Channel Deep Convolutional Neural Network Model for Random-Valued Impulse Noise Removal

Shaoping XU; Zhenyu LIN; Yan CUI; Ruirui LIU; Xiaohui YANG

doi:10.11999/JEIT190796

Volume 42 Issue 10

Oct. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(10): 2541-2548

Shaoping XU, Zhenyu LIN, Yan CUI, Ruirui LIU, Xiaohui YANG. A Dual-Channel Deep Convolutional Neural Network Model for Random-Valued Impulse Noise Removal[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2541-2548. doi: 10.11999/JEIT190796

Citation:

Shaoping XU, Zhenyu LIN, Yan CUI, Ruirui LIU, Xiaohui YANG. A Dual-Channel Deep Convolutional Neural Network Model for Random-Valued Impulse Noise Removal[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2541-2548. doi: 10.11999/JEIT190796

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A Dual-Channel Deep Convolutional Neural Network Model for Random-Valued Impulse Noise Removal

doi: 10.11999/JEIT190796

School of Information Engineering, Nanchang University, Nanchang 330031, China

Funds: The National Natural Science Foundation of China (61662044, 61163023), The Natural Science Foundation of Jiangxi Province (20171BAB202017)

Received Date: 2019-10-16
Rev Recd Date: 2020-07-20

Available Online: 2020-07-30

Publish Date: 2020-10-13

Abstract

Abstract

A Dual-channel Denoising Convolutional Neural Network (D-DnCNN) model for the removal of Random-Valued Impulse Noise (RVIN) is proposed. To obtain the reference image quickly, several Rank-Ordered Logarithmic absolute Difference (ROLD) statistics and one edge feature statistic are first extracted from a local window to construct a RVIN-aware feature vector which can describe the central pixel of the patch is RVIN or not. Next, a noise detector based on Deep Belief Network (DBN) is trained to map the extracted feature vectors to their corresponding noise labels to detect all noise-like pixels in the observed image. Then, under the guidance of noise labels, the Delaunay triangulation-based interpolation algorithm is exploited to restore all detected noise-like pixels quickly and generate a preliminary restored image used as reference image. Finally, the reference image and the noisy image are simultaneously fed into the D-DnCNN model to output its corresponding residual image, and the final restored image can be obtained by subtracting the residual image from the noisy image. Extensive experimental results show that, the denoising effect of the proposed D-DnCNN denoising model outperforms the existing state-of-art switching ones across a range of noise ratios, and it also works better than the ordinary single-channel DnCNN model.
- Image processing,
- Random-Valued Impulse Noise(RVIN),
- Dual-channel Denoising Convolutional Neural Network(D-DnCNN),
- Reference image,
- Noise-aware feature,
- Noise detection,
- Interpolation

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References(20)

References

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