Depth-based removal of thermal reflection with the light-field theory

Thermal imaging is a useful imaging technique in many scenarios. It can capture the temperature distribution of scenes in the dark and see through sparse smoke and dust. However, some surfaces such as steel and glass with high reflectivity lead to a reflection problem in thermal imaging, while heavy mist and gases lead to the occlusion problem. We proposed an efficient algorithm to solve the occlusion problem in our earlier work. The reflection in thermal images causes errors in detection and temperature measurement. Therefore, the precise model and efficient algorithms to solve this problem are in high demand. In this paper, we mainly model the reflection problem in thermal imaging and propose an algorithm to deal with it. In our experiments, a thermal camera array is built to capture the thermal light-field images. We first separate a part of the reflection pixels from thermal images based on the depth information. After that, the thermal reflection is removed by optimizing a designed cost function. The experiment results show that our reflection removal method can separate the thermal reflection with high precision, retain the objects in the scene, and get better performance than existing methods.