Modeling thermal infrared image degradation and real-world super-resolution under background thermal noise and streak interference

Thermal infrared image super-resolution technology successfully solves the problems of low resolution and blurred texture details in infrared images. However, the problem of background thermal noise and streak interference in thermal infrared images has not been effectively solved. Therefore, in this paper, we analyze and model the generation of background thermal noise and streak interference, and propose a real-world super-resolution algorithm based on generative adversarial network with multi-structure fusion. We first statistically analyze the imaging principle and dataset of the thermal imager to better model the phenomenon of background thermal noise and streak interference present in thermal infrared images. Meanwhile, in order to better recover the details, we use grayed-out visible images to guide the network training and propose a novel generator with multi-structural fusion. In the generator, we design a dynamic dense-attention module that dynamically assigns weights to the attention branch and the densely connected branch to take full advantage of both branches. Compared to other state-of-the-art methods, our proposed method exhibits excellent visual effects, effectively eliminating the effects of noise and streaks while enhancing image texture information.