Self-Supervised Infrared Image Denoising via Adaptive Gradient-Perception Network for FPN Suppression

Current denoising algorithms in infrared imaging systems predominantly target either high-frequency stripe noise or Gaussian noise independently, failing to adequately address the prevalent hybrid noise in real-world scenarios. To tackle this challenge, we propose a convolutional neural network (CNN)-based approach with a refined composite loss function, specifically designed for hybrid noise removal in raw infrared images. Our method employs a residual network backbone integrated with an adaptive weighting mechanism and edge-preserving loss, enabling joint modeling of multiple noise types while safeguarding structural edges. Unlike reference-based CNN denoising methods requiring clean images, our solution leverages intrinsic gradient variations within image sequences for adaptive smoothing, eliminating dependency on ground-truth data during training. Rigorous experiments conducted on three public datasets have demonstrated the optimal or suboptimal performance of our method in mixed noise suppression and detail preservation (PSNR > 32.13/SSIM > 0.8363).