A Ladder-Type Denoising Method

doi:10.13190/j.jbupt.2021-103

Journal of Beijing University of Posts and Telecommunications ›› 2022, Vol. 45 ›› Issue (1): 52-57.doi: 10.13190/j.jbupt.2021-103

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A Ladder-Type Denoising Method

WANG Jing¹, JIANG Zhuqing¹, MEN Aidong¹, GUO Xiaoqiang², WANG Zhikang³

1. School of Artifical Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Academy of Broadcasting Science, National Radio and Television Administration, Beijing 100866, China;
3. Faculty of Medicine Nursing and Health Sciences, Monash University, Melbourne 3800, Australia

Received:2021-05-23 Online:2022-02-28 Published:2021-12-16

Abstract

Abstract: A ladder-type denoising method is proposed to improve the denoising performance for raw red green blue(RAW) and standard red green blue(sRGB) real-world images. In the first stage, each channel of the noisy image is denoised separately utilizing intra-channel structure information. In the second stage, the inter-channel correlation information of the noisy image is utilized to further denoise the whole image, and the final boosted denoising result is obtained. Error feedback mechanism is introduced to reduce the information loss caused by sampling. Additionally residual dense connection makes features more effective for reuse and propagation; channel attention selectively enhances features with large amount of information and suppress useless features. The proposed method is compared with other denoising algorithms, and the results show that the proposed method achieves 49.55 dB peak signal to noise ratio(PSNR) in RAW images and 39.55 dB PSNR in sRGB images on Darmstadt noise dataset, and 39.52 dB PSNR on cross-channel dataset, which realizes competitive performance in comparison with other denoising algorithms.

Key words: convolution neural network, image denoising, error feedback, residual dense connection

CLC Number:

TP181

WANG Jing, JIANG Zhuqing, MEN Aidong, GUO Xiaoqiang, WANG Zhikang. A Ladder-Type Denoising Method[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(1): 52-57.

References

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A Ladder-Type Denoising Method

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