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- Title
基于深度学习的手术器械视觉图像高斯与椒盐噪声去除 方法研究.
- Authors
苗保明; 陈 炜; 吴 航; 余 明; 韩思齐
- Abstract
Objective To propose a deep learning-based method for removing Gaussian and pepper noises of the surgical instrument visual images so as to recover the detailed features of the images. Methods A light weight multi-task progressive network was constructed involving in a multi-feature fusion encoder-decoder network, an attention-guided network and a detail-recovery progressive network, which used the multi-feature fusion encoder-decoder network to predict and eliminate the noise information in the visual images, the attention-guided network to remove the residual noise and the detail-recovery progressive network to restore the underlying detail features of the denoised images. Some of the regular convolutions in the detail recovery progressive network were replaced with depth separable convolutions to realize lightweight design of the network constructed. Denoising experiments were conducted on the publicly available CBSD68 and Kodak 24 datasets and the self-constructed surgical instrument noise dataset so as to compare the denoising effects of the network constructed and the traditional methods and the classification accuracies of ResNet-18 model and Res Net-34 model for the denosied images by the network and to analyze computing power and memory usage before and after the light weight design. Results The network constructed gained better denoising effect than the classical methods for publicly available datasets, and ResNet-18 model and ResNet -34 model had higher accuracies when used to classify the images denoised by the network for the self constructed surgical instrument noise dataset. Lightweight design had the parameter number and floating point operations(FLOPs) decreased by approximately 27.27% and 29.81%, respectively. Conclusion The proposed light weight multi task progressive network behaves well in denoising surgical instrument visual images with reduced computating power consumption and memory usage.
- Publication
Chinese Medical Equipment Journal, 2024, Vol 45, Issue 2, p1
- ISSN
1003-8868
- Publication type
Article
- DOI
10.19745/j.1003-8868.2024021