Thermal Microscopic Imaging based on Diffusion Models for Super-resolution Inspection

Existing infrared micro-thermal imaging often faces low resolution and contrast issue, which hinder its applications in high-precision integrated circuit inspection. Based on the advanced super-resolution techniques, high-resolution infrared images can be reconstructed from low-resolution measurements. Recently, diffusion models have shown strong capacity in the image reconstruction tasks. However, due to the low signal-to-noise ratio and inferior contrast, directly applying the traditional diffusion models to the infrared image is hardly to obtain high-quality super-resolution results. In this work, we first design a degradation model for real infrared imaging systems, and then we design an infrared super-resolution diffusion model based on ResShift, which introduces uncertainty into the u-net to enhance the robustness of the model and the reconstruction quality of the infrared images. The proposed method is validated based on a set of numerical experiments.