| 聂泽西,王洪金,何赟泽,付玉轩,张振军.尘雾天气条件下退化图像时空频域恢复与增强[J].电子测量与仪器学报,2026,40(2):86-94 |
| 尘雾天气条件下退化图像时空频域恢复与增强 |
| Spatiotemporal-frequency domain restoration and enhancement fordegraded images in multi-weather scenarios |
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| DOI： |
| 中文关键词: 图像恢复与增强 傅里叶变换 核函数 伪时间序列 |
| 英文关键词:image restoration and enhancement fourier transform kernel function pseudo time series |
| 基金项目:湖南省自然科学基金重大项目(2021JC0004)、中国国家自然科学基金一般项目(62373138)、国家磁约束核聚变能发展研究专项青年项目（2024YFE03250600）、教育部系统控制与信息处理重点实验室开放项目(Scip20230107)、湖南省科技创新领军项目(2023RC1039)资助 |
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| Author | Institution |
| Nie Zexi | 1.College of Electrical and Information Engineering, Hunan University, Changsha 410082,China;
2.School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082,China |
| Wang Hongjin | College of Electrical and Information Engineering, Hunan University, Changsha 410082,China |
| He Yunze | 1.College of Electrical and Information Engineering, Hunan University, Changsha 410082,China; 3.Shenzhen Research Institute of Hunan University, Shenzhen 518000,China |
| Fu Yuxuan | College of Electrical and Information Engineering, Hunan University, Changsha 410082,China |
| Zhang Zhenjun | School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082,China |
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| 中文摘要: |
| 在计算机视觉任务中，尘、雾环境对图像的能见度及细节特征造成了严重的影响，制约了下游视觉任务的性能，为恢复和增强由恶劣天气条件退化的图像细节，提出一种时空频域图像恢复增强方法。该方法通过研究光在尘、雾大气条件下的扩散数学模型，用高斯滤波模拟大气对光传播的扩散衰减作用，从退化的输入构建了伪时间图像序列，并通过时空维度的傅里叶变换得到序列的时空频域特征。受还原赝热流（RPHF）理论的启发，设计了频域反卷积核对序列的高频信息进行加权以抵消大气扩散对图像细节信息的退化效应，对加权之后的频率特征进行傅里叶反变换以重建增强图像。为了验证该方法，建立了包含不同退化强度的尘、雾场景天气数据集，并对其进行实验。实验结果表明，与传统算法相比，该方法在中、重度退化场景下（如雾-重度退化的可见边缘比e: 78.990）表现较优秀，能够较有效地恢复图像细节。然而，在轻度退化场景下，由于图像的高频信息较多，方法对于图像高频信息不加区分地放大对图像质量的恢复产生了负面作用。总体而言，该方法更适用于中至重度退化图像的恢复与增强。 |
| 英文摘要: |
| In computer vision tasks, dust and fog environments have a severe impact on the visibility and detailed features of images, which restricts the performance of downstream vision tasks. To restore and enhance the details of images degraded by adverse weather conditions, a spatio-temporal frequency domain image restoration and enhancement method is proposed. This method studies the mathematical model of light diffusion under dust and fog atmospheric conditions, uses Gaussian filtering to simulate the diffusion and attenuation effect of the atmosphere on light propagation, constructs a pseudo-time image sequence from the degraded input, and obtains the spatio-temporal frequency domain features of the sequence through Fourier transform in the spatio-temporal dimension. Inspired by the
restored pseudo heat flux (RPHF) theory, a frequency domain deconvolution kernel is designed to weight the high-frequency information of the sequence to counteract the degradation effect of atmospheric diffusion on the image detail information. The inverse Fourier transform is performed on the weighted frequency features to reconstruct and enhance the image. To verify this method, a weather dataset containing dust and fog scenes with different degradation intensities is established for experiments. The experimental results show that compared with traditional algorithms, this method performs excellently in medium and severe degradation scenarios (such as the visible edge ratio eof severely degraded fog: 78.990) and can effectively restore images. However, in mild degradation scenarios, due to the large amount of high-frequency information in the images, the indiscriminate amplification of high-frequency information by the method has a negative effect on image quality restoration. Overall, this method is more suitable for the restoration and enhancement of moderately to severely degraded images. |
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