Laser & Optoelectronics Progress, Volume. 60, Issue 14, 1410014(2023)
Micro-CT Image Denoising Algorithm Based on Deep Residual Encoding-Decoding
Aiming at the problem that noise will be generated during micro-CT scanning in the laboratory, resulting in the decline of CT image quality after reconstruction, this paper proposes a deep multi-residual encoding-decoding convolutional denoising network. This method is based on the original residual encoding-decoding convolutional network. First, we increased the number of convolutional layers and introduced the multiple residuals to realize effective learning of noise distribution characteristics in lab-level micro-CT images. Second, a special mix-loss function was designed to strengthen the network's ability to retain image details. Experimental results show that the proposed method has a significant effect on noise suppression and can greatly preserve the structure and feature information of CT images.
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Huijuan Fu, Xiaoqi Xi, Yu Han, Lei Li, Xinguang Wang, Bin Yan. Micro-CT Image Denoising Algorithm Based on Deep Residual Encoding-Decoding[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1410014
Category: Image Processing
Received: Jun. 6, 2022
Accepted: Sep. 13, 2022
Published Online: Jul. 17, 2023
The Author Email: Yan Bin (ybspace@hotmail.net)