Infrared Image Super Resolution Method Based on Stochastic Degradation Modeling

Infrared images hold significant application value in fields such as military reconnaissance, security surveillance, and medical diagnosis. However, issues like low resolution, high noise, and complex degradation characteristics severely hinder their practical application effectiveness. This paper introduces an infrared super-resolution reconstruction algorithm based on a random degradation model and Generative Adversarial Networks (GANs), addressing the diversity of infrared image degradation processes. The primary contribution lies in explicitly modeling key degradation parameters of infrared images (such as blur kernel and noise distribution) using a random degradation model, generating diverse low-resolution to high-resolution image pairs, and significantly enhancing the model’s generalization ability for complex degradations. Experiments on the Airo infrared dataset and a self-built infrared dataset demonstrate that when the resolution is increased by a factor of 2, 4, and 8, the 4× resolution reconstructed images exhibit notable advantages in terms of texture detail and noise suppression. Especially in 4× super-resolution reconstruction, compared to three typical deep learning algorithms, our algorithm improves the Peak Signal-to-Noise Ratio (PSNR) by 3.046 dB, 1.8489 dB, and 0.2108 dB, respectively, and the Structural Similarity Index (SSIM) by 0.0387 (4.76%), 0.0287 (3.48%), and 0.0131 (1.56%), respectively, with perceptual similarity decreasing by 0.2465, 0.13344, and 0.0514 (lower values indicate better perceptual quality). Subjective visual assessments further validate the algorithm’s significant advantages in noise reduction and weak texture restoration. This study proposes an infrared image super-resolution reconstruction method based on random degradation modeling, which holds significant theoretical and practical value in complex infrared degradation scenarios.