计算机与数字工程2023，Vol.51Issue(10)：2400-2404,2417,6.DOI:10.3969/j.issn.1672-9722.2023.10.035
基于卷积神经网络的多尺度特征融合去噪算法
Multiscale Feature Fusion Denoising Algorithm Based on Convolution Neural Network
摘要
Abstract
Most of the existing convolutional neural network denoising algorithms can only perform well in synthetic noise,and only extract features from a single scale,which can not reconstruct a cleaner image.To solve the above problems,a multi-scale feature fusion denoising algorithm based on convolution neural network is proposed.The algorithm uses layered features of different scales to obtain more receptive fields.Through feature fusion,the features of the previous scale are fused to the current scale for pro-gressive training,which can remove more noise.A encode-decode is set after the feature extraction of each layer,and hole convolu-tion is added to the encode-decode to prevent image detail damage and information loss when the image resolution is too low.The proposed network model is tested on synthetic noise data set and real noise data set.The results show that the denoising performance of the algorithm is better than the comparison algorithms and can retain more details.关键词
图像处理/卷积神经网络/多尺度特征/特征融合/真实噪声图像去噪Key words
image processing/convolutional neural network/multiscale features/feature fusion/real-world noisy image de-noising分类
信息技术与安全科学引用本文复制引用
许雪,郭业才,李晨..基于卷积神经网络的多尺度特征融合去噪算法[J].计算机与数字工程,2023,51(10):2400-2404,2417,6.基金项目
国家自然科学基金项目(编号:61673222) （编号:61673222）
无锡学院人才启动经费项目(编号:550221028)资助. （编号:550221028）
