%0 Journal Article
%A GUO Xiaoqiang
%A JIANG Zhuqing
%A MEN Aidong
%A WANG Zhikang
%A WANG Jing
%T A Ladder-Type Denoising Method
%D 2022
%R 10.13190/j.jbupt.2021-103
%J Journal of Beijing University of Posts and Telecommunications
%P 52-57
%V 45
%N 1
%X 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.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-103