A multi-image super-resolution algorithm applied to thermal imagery

Although thermal imaging is a widely used technique in many applications and is under continuous development, one of its limits is the relatively coarse spatial resolution. Nevertheless, in the last years, a number of super-resolution algorithms have been developed which allow to enhance the resolution of the images. They can be divided in two main different categories: single-image or multiple image-based algorithms. In this work, a multiple image-based algorithm for the super-resolution was implemented, tested and applied to terrestrial thermal imaging with the aim to overcome the limitation of the low resolution. In particular, the method relies on the use of many images acquired from slightly different positions to obtain, thanks to the redundancy of observations, a super-resolution frame having an upsampling factor of four. Several tests were performed on synthetic datasets, and the accuracy of the obtained super-resolution images was investigated. Moreover, an original algorithm capable to identify gross errors during the image registration phase, which is one of the crucial phases, is presented and its reliability assessed. Results showed the effectiveness of the proposed method on both common visible images and thermal infrared ones, since discrepancies between reconstructed and reference values are reduced by 18 and 25% respectively, when compared with a conventional bicubic algorithm. Finally, the proposed method was tested on a case study concerning the thermal survey of the façade of a historical building in Bologna (Palazzo D’Accursio). A dataset of real thermal frames was acquired and a super-resolution image of the subject was generated through the developed algorithm. Strengths and weaknesses of the method were analysed and discussed in the paper.