Restoration of Atmospheric Turbulence Degraded Images Based on Physics-Inspired Deep Neural Networks
泽 原 · Advances in Applied Mathematics · 2025
大气湍流会导致大气光传输数据模糊和扭曲，影响成像质量和后续分析处理。为了更好地恢复由大气湍流所影响的失真图像，本研究基于物理启发式深度神经网络提出了一种大气湍流退化图像复原算法。通过结合金字塔结构和注意力机制，提高特征提取的精度，从而改善复原图像质量。通过湍流模拟器模拟退化图像，提取大气湍流退化图像的先验信息，提升复原效果。实验结果表明，本研究在处理处理弱和中湍流环境下的大气光传输数据时复原能有效提高成像的视觉质量，降低成像的模糊和几何畸变程度。Atmospheric turbulence causes blurring and distortion in atmospheric optical transmission data, affecting imaging quality and subsequent analysis and processing. To better restore distorted images influenced by atmospheric turbulence, this study proposes an atmospheric turbulence degradation image restoration algorithm based on a physics-inspired deep neural network. By combining a pyramid structure and attention mechanisms, the accuracy of feature extraction is enhanced, thereby improving the quality of the restored images. Degraded images are simulated using a turbulence simulator, and prior information about atmospheric turbulence degradation is extracted to improve the restoration effect. Experimental results show that this study effectively enhances the visual quality of imaging and reduces blur and geometric distortion in atmospheric optical transmission data under weak and moderate turbulence environments.