Deep learning-based Gaussian and pepper noise removal method for visual images of surgical instruments
10.19745/j.1003-8868.2024021
- VernacularTitle: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
- Author:
Bao-Ming MIAO
1
,
2
,
3
;
Wei CHEN
;
Hang WU
;
Ming YU
;
Si-Qi HAN
Author Information
1. 天津理工大学天津市先进机电系统设计与智能控制重点实验室,天津 300384
2. 天津理工大学机电工程国家级实验教学示范中心,天津 300384
3. 军事科学院系统工程研究院,天津 300161
- Keywords:
deep learning;
surgical instrument;
visual image;
image denoising;
Gaussian noise;
pepper noise
- From:
Chinese Medical Equipment Journal
2024;45(2):1-7
- CountryChina
- Language:Chinese
-
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 lightweight 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 Kodak24 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 ResNet-34 model for the denosied images by the network and to analyze computing power and memory usage before and after the lightweight 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 lightweight multi-task progressive network behaves well in denoising surgical instrument visual images with reduced computating power consump-tion and memory usage.[Chinese Medical Equipment Journal,2024,45(2):1-7]
