# Multi-Scale Ensemble Learning for Thermal Image Enhancement
## 2 Background  
Thermal image enhancement can be largely categorized into three topics: detail enhancement (super-resolution), noise reduction, and contrast enhancement. Firstly, the detail enhancement of a thermal image is done to improve a low-quality thermal image and turn it into a high-quality one. [PERSON] et al. [10] suggested a convolutional neural network-based super-resolution approach. Through the comparison between networks using gray and midwave infrared images, they found that the network using gray images showed better performance than the one using midwave infrared images. In addition, they verified that the improvement in the quality of a thermal image led to the performance enhancement of the applications. [PERSON] et al. [11] presented a residual network for thermal image enhancement. They experimentally verified that the brightness domain is best suited for training a network for thermal image enhancement. This method generates a high-quality thermal image by element-wise summation of low-quality input and residual output images. [PERSON] et al. [12] introduced the networks trained by visual and thermal images that were based on [31]. They also released a public dataset that has 101 thermal images acquired using a TAU2 thermal camera. [PERSON] and [PERSON] [13] proposed a hierarchical edge-based guided super-resolution method. This method needs visible-range images to extract multi-level edge information. [PERSON] et al. [14] suggested the CycleGAN-based [32] super-resolution method. This method considers the scale factor \(\times 2\) in two different scenarios. The first scenario is to generate mid-resolution images from low-resolution images. Secondly, high-resolution images are generated from mid-resolution images. Furthermore, the authors released thermal datasets captured with three different devices for low-resolution, mid-resolution, and high-resolution thermal images. [PERSON] et al. [15] presented a super-resolution algorithm for aerial and terrestrial thermal images, which was based on total variation regularization. This method is a fully automatic method with a low-cost adaptive rule. In addition, they introduced a new thermal image quality metric based on a specific region of interest for radiometric analysis. Then, to reduce the noise of a thermal image, [PERSON] et al. [17] used spectral and spatial filters with a two-stage strategy. They analyzed the noise pattern and improved the level of stripe non-uniformity correction while preserving the details. [PERSON] and [PERSON] [18] suggested a de-striping convolutional neural network based on a double-branched structure using a parametric noise model. The parametric fixed pattern noise model was built through diagnostic experiments of infrared images using the physical principle of the infrared detector and the signal response. The model parameters were optimized using measurement data collected over a wide range of detector temperatures. They also generated the training data using the models to ensure stable performance for various patterns. Lastly, for the contrast enhancement of a thermal image, [PERSON] and [PERSON] [20] introduced the histogram equalization-based method. This method increases the global contrast by distributing the thermal histogram almost evenly. [PERSON] et al. [21] adopted a multi-scale top-hat transform. [PERSON] et al. [23] presented a conditional generative adversarial network-based method trained on visible images. This approach has two elements, a generative sub-network and a conditional discriminative sub-network. As mentioned above, various research topics have been studied, including detail enhancement, contrast enhancement, and noise reduction. Contrast enhancement is a method for improving the quality of an image considering that human visual perception is more sensitive to the changes in contrast than the changes in brightness.