## 1 Introduction
Later, different Deep Neural Networks (DNNs) based solutions have been introduced to further improve the restoration quality (or perception). In [30] [PERSON] et al. introduced a novel Deeply Recursive Convolutional Network (DRCN) model. It utilizes a skip connection correlating a LR input with a high resolution (HR) reference data and uses recursive supervision to minimize the exploding/vanishing gradients problem. Other improvements to SR include the application of residual mappings and gradient clipping (Deeply Recursive Residual Network (DRRN) [31]), the use of attention networks [32], the application of multi-scale residual hierarchical networks [33], etc. The very good results have also been obtained using SR algorithms based on generative networks [6; 34; 35; 36]. DNN-based algorithms have also been applied to process thermal, infrared images. [PERSON] et al. [37] proposed simple CNN-based models to classify different raw materials from short range thermal images. Other state-of-the-art CNN models were used in different thermal imaging applications,e.g., SqueezeNet based Thermalnet [38], denoising CNN [39], etc. Some authors applied DNN models to improve a thermal image contrast. For example, in [40] authors proposed a conditional GAN-based network to address the thermal imaging contrast enhancements. The experiments performed on thermal images obtained for indoor and outdoor scenes showed much better results in comparison to traditional histogram equalization techniques and other image transformation methods. Only limited number of papers present the results of SR algorithms applied to thermal images. In [41] authors proposed a cascaded architecture of deep networks with multiple receptive fields. They investigated the large-scale resolution improvement (8\(\times\)) using thermal images of indoor and outdoor scenes. However, the training dataset for the proposed cascaded deep networks was very small (100 images). In [42] authors applied the Comprehensive Sensing (CS) Theory to the SRCNN-like model structure to improve high frequency information and alleviate some fixed pattern noise present in the output from CS. However, the model was trained using 400 visible light images and tested only on 6 (outdoor) thermal images. In this paper, we combine the use of super resolution algorithms for thermal image sequences of faces to investigate a possible accuracy improvement in the respiratory rate estimation task. Our work is probably the first attempt to the improvement of contactless RR estimation using SR DNN specifically designed for thermal data, since most of previous solutions focus on visible light images that have different characteristics than thermal images. Our model is characterized by a wider receptive field which allows for taking into account more distant relations between neighbouring facial areas due to the blurring effect in thermal images. Some attempt to this problem has been done by us in our earlier preliminary work [43], where we showed that the use of SR DNNs can improve the RR estimation accuracy. Yet, in this previous study we only analysed one RR estimator on a single dataset. To avoid being biased by a specific method and data, in this study, we extend our experiments to different RR estimators and evaluate the proposed solution on two thermal datasets collected by us for 71 volunteers.