计算机工程2024，Vol.50Issue(2)：273-280,8.DOI:10.19678/j.issn.1000-3428.0067130
基于先验驱动深度神经网络的泊松去噪变分模型
Poisson Denoising Variational Model Based on Prior-Driven Deep Neural Network
摘要
Abstract
The denoising of Poisson noise images is a typical ill-conditioned inverse problem.Its variational model requires repeated iterations and parameter adjustments,which is less computationally efficient.Pure deep learning models often draw on experience to design networks,but they have poor interpretability.Based on the Alternating Direction Method of Multipliers(ADMM)expansion of the Poisson noise denoising variational model,an end-to-end Deep Convolutional Neural Network(DCNN)is designed to derive an improved Poisson denoising variational model by combining the Poisson noise distribution data with the Bayesian maximum a posteriori probability estimation.To solve the Poisson denoising energy function extremum problem,ADMM is used,which introduces auxiliary variables,Lagrange multipliers,and penalty parameters and decomposes the problem into two alternating optimization subproblems of Gaussian denoising and image reconstruction.First,Gaussian denoising is achieved using the priori-driven DCNN to learn the Gaussian denoising.Next,the image reconstruction is completed via analytical iteration.The experimental results show that compared with the NonLinear Principal Component Analysis(NLPCA),VST+BM3D,I+VST+BM3D,and TRDPD-based Poisson denoising models,the mean values of the Peak Signal-to-Noise Ratio(PSNR)of the model on the Set12 dataset are improved by 2.73,0.87,0.57,and 0.50 dB,respectively,and the mean values of the Structural SIMilarities(SSIM)are improved by 0.148,0.046,0.020,and 0.047,respectively.The Poisson denoising effects on color images and Positron Emission Tomography/Computed Tomography(PET/CT)images are significantly improved.The above experimental results prove that the model effectively removes the Poisson noise and prevents the problems of artifacts and scattering generated during the Poisson denoising process.关键词
泊松去噪/卷积神经网络/去噪先验/变分模型/交替方向乘子法Key words
Poisson denoising/Convolutional Neural Network(CNN)/denoising prior/variational model/Alternating Direction Method of Multipliers(ADMM)分类
信息技术与安全科学引用本文复制引用
李倩,魏伟波,杨光宇,宋金涛,孙璐,潘振宽..基于先验驱动深度神经网络的泊松去噪变分模型[J].计算机工程,2024,50(2):273-280,8.基金项目
国家自然科学基金(61772294) （61772294）
山东省自然科学基金联合基金(ZR2019LZH002) （ZR2019LZH002）
山东省高等学校"青创科技计划"创新团队(2021RW018). （2021RW018）
