Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning
This work addresses infrared image deturbulence for applications like surveillance or remote sensing, representing an incremental advance by integrating learned degradation parameters into a wide & deep architecture.
The paper tackles infrared image restoration under turbulence by proposing DparNet, a multi-frame network that learns degradation parameters from degraded images to modulate restoration adaptively, achieving PSNR improvements of 0.6-1.1 dB with minimal increases in model parameters and computational complexity.
Infrared images captured under turbulent conditions are degraded by complex geometric distortions and blur. We address infrared deturbulence as an image restoration task, proposing DparNet, a parameter-assisted multi-frame network with a wide & deep architecture. DparNet learns a degradation prior (key parameter matrix) directly from degraded images without external knowledge. Its wide & deep architecture uses these learned parameters to directly modulate restoration, achieving spatially and intensity adaptive results. Evaluated on dedicated infrared deturbulence (49,744 images) and visible image denoising (109,536 images) datasets, DparNet significantly outperforms State-of-the-Art (SOTA) methods in restoration performance and efficiency. Notably, leveraging these parameters improves PSNR by 0.6-1.1 dB with less than 2% increase in model parameters and computational complexity. Our work demonstrates that degraded images hide key degradation information that can be learned and utilized to boost adaptive image restoration.