The Generating Super Resolution of Thermal Image based on Deep Learning - Ismail Ismail; Yefriadi Y; Yuhefizar Y; Fibriyanti; Zulka Hendri (Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022)
The need for a high resolution to the thermal image is urgent and essential. The high resolution of the thermal image can give accurate information on the heat distribution map of the objects. The accurate heat distribution maps can give accurate temperature information. This accurate temperature measurement is used for measuring many objects such as electric motors, engines, the human body, and so on—this information is used to detect the anomalies of the object to find the damaged parts. The anomalies are considered damaged parts found in solar panels, agricultural fields, buildings, bridges, etc. As the super-resolution of thermal images is very important, generating them is compulsory. The camera for obtaining super-resolution thermal images is rare, not available in the common market. Furthermore, this kind of device is costly too. Therefore not all the users, such as farmers or technicians, can have them. In order to handle the problem, the proposed method has the purpose of generating super-resolution thermal images economically and is more accessible through the deep learning method. The dataset is taken from the solar panel. The results show that the proposed method can handle the low-resolution problem of thermal images.