Efficient Aero-Optical Degraded Image Restoration via Adaptive Frequency Selection

During high-speed flight, the aircraft causes rapid compression of the surrounding air, creating a complex turbulent flow field. This high-speed flow field interferes with the optical transmission of optical imaging systems, resulting in high-frequency random displacement, blurring, intensity attenuation, or saturation of the target scene. Aero-optical effects severely degrade imaging quality and target recognition capabilities. Based on the spectral characteristics of aero-optical degraded images and the deep learning approach, this paper proposes an adaptive frequency selection network (AFS-NET) for correction. To learn multi-scale and accurate features, we develop cascaded global and local attention mechanism modules to capture long-distance dependency and extensive contextual information. To deeply excavate the frequency component, an adaptive frequency separation and fusion strategy is proposed to guide the image restoration. Integrating both spatial and frequency domain processing and learning the residual representation between the observed data and the underlying ideal data, the proposed method assists in restoring aero-optical degraded images and significantly improves the quality and efficiency of image reconstruction.