As modern displays continue to increase in resolution, means of capturing images and videos at such high resolutions can be prohibitively expensive. This is especially true in the infrared domain. Image super-resolution, or upsampling, has often been applied to improve the aforementioned problem. Deep learning models have been proposed to reconstruct high quality high-resolution images from a low-resolution base. Previous solutions require a massive number of parameters which necessitate a large amount of free memory and computation power or they fall apart when applied to the infrared domain. As a result, many modern super-resolution models are not entirely practical. One difficult aspect in IR super-resolution is that IR images are inherently noisy, causing a poor signal-to-noise ratio, due to characteristics of IR sensors and internal reflections within the lenses. Because of this, super-resolution in IR must also act somewhat as a denoiser. Therefore, we propose a highly efficient, super-resolution model capable of producing single-image super-resolution in the IR domain.
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