基于改进U型网络的火焰光场图像降噪及温度场重建  

Noise Reduction and Temperature Field Reconstruction of Flame Light Field Images Based on Improved U-network

作　　者：单良 孙健 洪波 孔明 SHAN Liang;SUN Jian;HONG Bo;KONG Ming(Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,College of Information Engineering,China Jiliang University,Hangzhou 310018,China;College of Metrology and Measurement Engineering,China Jiliang University,Hangzhou 310018,China)

机构地区：[1]中国计量大学信息工程学院浙江省电磁波信息技术与计量检测重点实验室,杭州310018 [2]中国计量大学计量测试工程学院,杭州310018

出　　处：《电子与信息学报》2025年第3期792-802,共11页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(51874264,52076200),中央引导地方科技发展资金项目(2023ZY1008)。

摘　　要：火焰光场图像在形成过程中夹杂的辐射噪声和成像噪声会降低火焰温度场3维重建精度,该文提出一种基于改进U型网络(UNet)的降噪模型,该模型针对辐射噪声和成像噪声的特性以及复杂火焰图像的纹理信息设计了背景净化模块和边缘信息优化模块。通过密集卷积操作对图像背景层进行特征提取,着重净化夹杂在图像背景层的辐射噪声。通过UNet模块中对称的编码器-解码器网络结构和跳跃连接,对通道间的辐射噪声和表层的成像噪声降噪。最后利用边缘优化模块对图像细节信息进行提取,从而获得更高质量的火焰光场图像。数值模拟部分,在火焰光场图像上混合加入信噪比为10 dB的辐射噪声和成像噪声,经该文模型降噪后的峰值信噪比(PSNR)和结构相似指数(SSIM)高达47 dB和0.9931,与其他降噪模型相比有明显优势。随后,将火焰光场图像先经该文降噪模型降噪,再进行温度场重建,测得重建平均相对误差比未降噪时降低了约37%~57%,明显提升了火焰温度场3维重建的精度。实验部分,获取真实蜡烛火焰和丁烷火焰光场图像,经该文降噪模型降噪后的蜡烛火焰图像SSIM高达0.9870,降噪后的丁烷燃烧火焰图像SSIM为0.9808。Objective This study establishes the nonlinear relationship between flame light field images and the 3D temperature field using deep learning techniques,enabling rapid 3D reconstruction of the flame temperature field.However,light-field images are prone to radiation and imaging noise during transmission and imaging,which significantly degrades image quality and reduces the accuracy of temperature field reconstruction.Therefore,denoising of flame light field images,with maximum preservation of texture and edge details,is critical for high-precision 3D reconstruction.Deep learning-based denoising algorithms are capable of accommodating a broad range of noise distributions and are particularly effective in enhancing texture and contour information without requiring extensive prior knowledge.Given the complexity of noise in flame light field images,deep learning methods present an optimal solution for denoising.Methods This paper presents a denoising model based on an improved UNet network,designed to address radiation and imaging noise,as well as the texture information in complex flame images.The model reduces noise and optimizes the flame light field image through three modules:the background purification module,the UNet denoising module,and the edge optimization module.Feature extraction is performed on the image background layer using dense convolution operations,with a focus on purifying the radiated noise embedded in the background.The symmetrical encoder-decoder network structure and skip connections in the UNet module help to reduce both radiation noise between channels and imaging noise on the surface.The edge optimization module is tailored to extract detailed information from the image,aiming to enhance the quality of the flame light field images.Comparative and ablation experiments confirm the superior noise reduction performance and effectiveness of the proposed modules.Results and Discussions In the numerical simulation,radiation noise and imaging noise are added to the flame light field image,generati

关 键 词：图像处理 图像降噪 深度学习 火焰光场图像 3维温度场重建 

分 类 号：TN919.81[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]