基于残差密集卷积自编码的高噪声图像去噪方法
Residual Dense Convolutional Autoencoder for High Noise Image Denoising
张杰 1卢淼鑫 1李嘉康 2徐大勇 2黄雯潇 1史小平3
作者信息
- 1. 郑州轻工业大学电气信息工程学院 郑州 450002
- 2. 中国烟草总公司郑州烟草研究院烟草工艺重点实验室 郑州 450000
- 3. 哈尔滨工业大学控制与仿真中心 哈尔滨 150080
- 折叠
摘要
在高噪声图像去噪中,传统卷积自编码器难以挖掘有效的深度特征信息,进而影响了图像的重建质量.为了提高高噪声图像的重建质量,提出了一种残差密集卷积自编码器网络模型.该模型首先使用卷积操作代替池化操作以提高高噪声图像的表征能力;同时,在编码和解码阶段设计三级密集残差网络结构,实现图像特征的有效挖掘;最后,设计一个优化损失函数以进一步提高重建图像的质量.实验结果表明,设计的去噪方法能够从高噪声图像中重建高质量的图像,同时能够保留更多的细节特征信息,有效验证了该算法在图像去噪中的有效性.该方法能够有效解决高噪声图像的去噪问题,具有重要的应用价值.
Abstract
In the field of high noise image denoising,traditional convolutional auto-encoders face challenges in extracting meaning-ful depth feature information,resulting in poor image reconstruction quality.To address this issue and improve the reconstruction quality of high noise images,this paper proposes a residual-density convolutional auto-encoder network model.The model firstly uses convolutional operations instead of pooling operations to improve the characterisation of high noise images.Moreover,a three-stage dense residual network structure is designed for effective image feature mining during the coding and decoding stages.Finally,an optimised loss function is designed to further improve the quality of the reconstructed images.Experimental results show that the denoising method presented in this paper is capable of reconstructing high quality images from high noise images while preserving more detailed feature information.It confirms the effectiveness of the algorithm in image denoising.The pro-posed method effectively addresses the challenge of denoising high noise images and has significant practical value.
关键词
图像去噪/卷积自编码器/残差密集卷积/高噪声图像/优化损失函数Key words
Image denoising/Convolutional autoencoder/Residual dense convolution/High noise image/Optimized loss function引用本文复制引用
基金项目
国家自然科学基金(62102373)
国家自然科学基金(62006213)
河南省科技攻关计划(222102320321)
河南省科技攻关计划(232102220020)
出版年
2024
NETL
NSTL
万方数据