Optimizing super-resolution reconstruction using a genetic algorithm

Super-resolution reconstruction (SRR) is aimed at increasing spatial resolution given a single image or multiple images presenting the same scene. The existing methods are underpinned with a premise that the observed low resolution images are obtained from a hypothetic high resolution image by applying a certain imaging model (IM) which degrades the image and decreases its resolution. Hence, the reconstruction consists in applying an inverse IM to recover the high resolution data. Such an approach has been found effective, if the IM is known and controlled, in particular when the low resolution images are indeed obtained from a high resolution one. However, in a real-world scenario, when SRR is performed from images originally captured at low resolution, finding appropriate IM and tuning its hyperparameters is a challenging task. In this paper, we propose to optimize the SRR hyperparameters using a genetic algorithm, which has not been reported in the literature so far. We argue that this may substantially improve the capacities of learning the relation between low and high resolution images. Our initial, yet highly encouraging, experimental results reported in the paper allow us to outline our research pathways to deploy the developed techniques in practice.