Image denoising based on edge enhancement and convolutional neural network

Jing Liu; Yu Dong; Xiaoyan Zhang

doi:10.1117/12.2599834

30 June 2021 Image denoising based on edge enhancement and convolutional neural network

Jing Liu, Yu Dong, Xiaoyan Zhang

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187813 (2021) https://doi.org/10.1117/12.2599834
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

The image denoising methods based on convolutional neural network can remove the noise better. However, some detailed features will be removed along with the noise, resulting in the image blurred. In this paper, we propose a denoising network based on edge enhancement and residual learning to make it generate higher quality denoised results. First, we use the Non-local Means (NLM) denoising algorithm to pre-denoise the noisy image, and then use canny edge detection on this result to obtain the corresponding edge matrix; Second, perform non-subsampled contourlet transform (NSCT) decomposition on the noisy image to obtain high-frequency sub-bands; Third, locate and amplify the edge coefficients of the high-frequency sub-bands through the edge matrix to obtain the edge-enhanced noise image; Finally, the edge-enhanced noise image is input into the residual denoising network training to get the final denoised result. The experiments show that when compared with the traditional denoising methods, our proposed method can remove more noise while retaining more rich edge information and have a better improvement in visual quality.

Citation Download Citation

Jing Liu, Yu Dong, and Xiaoyan Zhang "Image denoising based on edge enhancement and convolutional neural network", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187813 (30 June 2021); https://doi.org/10.1117/12.2599834

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