计算机技术与发展2025，Vol.35Issue(10)：43-52,10.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0138
基于自编码器和稀疏学习的深度鲁棒图像重构
Deep Robust Image Reconstruction Based on Autoencoder and Sparse Learning
摘要
Abstract
To address the issue of poor image reconstruction performance in noisy environments caused by L2 loss and L2 regularization in existing autoencoders,we propose a novel deep robust image reconstruction method based on a Smoothed L1 Autoencoder and Sparse Feature Learning(SL1AE-SFL),which significantly enhances reconstruction quality and performance.Firstly,we introduce the Smoothed L1 Autoencoder,which replaces the traditional L2 loss function and ReLU activation function with the L1 loss function and smoothed sReLU activation function,thereby improving the model's robustness against noises and outliers.Secondly,to tackle the challenge of L2 regularization failing to effectively suppress the influence of noisy features,we propose a robust autoencoder approach based on sparse feature learning,including SL1AE-SFL(L1)and SL1AE-SFL(L21).By imposing sparse regularization constraints based on L1 and L21 norms,the model becomes less affected by noisy features and focuses more on essential features,thereby significantly improving reconstruction accuracy.Finally,we propose an efficient optimization algorithm based on proximal gradient descent,which effectively alleviates the training inefficiency of L1-norm and L21-norm based sparse regularizations.Experimental results on multiple datasets demonstrate that SL1AE-SFL outperforms other methods in terms of reconstruction error and clustering accuracy.Moreover,we analyze the impact of network architecture on reconstruction quality,and ablation studies show that appropriately increasing the number of network layers can further enhance image reconstruction performance,validating the effectiveness of deep networks in feature extraction and image reconstruction.关键词
图像重构/深度鲁棒模型/无监督学习/平滑L1自编码器/稀疏特征学习/近端梯度下降Key words
image reconstruction/deep robust models/unsupervised learning/smoothed L1 autoencoder/sparse feature learning/proximal gradient descent分类
信息技术与安全科学引用本文复制引用
张俊洋,明镝..基于自编码器和稀疏学习的深度鲁棒图像重构[J].计算机技术与发展,2025,35(10):43-52,10.基金项目
国家自然科学基金(62441607) （62441607）
重庆市自然科学基金面上项目(CSTB2024NSCQ-MSX0341) （CSTB2024NSCQ-MSX0341）
重庆市教委科学技术研究项目(KJQN202301142) （KJQN202301142）
重庆理工大学科研启动基金资助项目(2022ZDZ026) （2022ZDZ026）
