Leveraging Multi Scale Backbone With Multilevel Supervision for Thermal Image Super Resolution
This paper proposes an attention based multi-level model with multi-scale backbone for thermal image super-resolution. The model leverages the multi-scale backbone as well. The thermal image dataset is provided by PBVS 2020 in their thermal image super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [??]. However, only the medium and high resolution images are used to train to train the proposed architecture to generate the super-resolution images in x2, x4 scales. The proposed architecture is based on the Res2net blocks as the backbone of the network. Along with this, the coordinate convolution layer and a dual attention are also used in the architecture. Further, the multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [??]. The results show that our model is efficient to achieve the state of art results on the PBVS dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.