Towards Evolutionary Super-Resolution

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)


Super-resolution reconstruction (SRR) allows for producing a high-resolution (HR) image from a set of low-resolution (LR) observations. The majority of existing methods require tuning a number of hyper-parameters which control the reconstruction process and configure the imaging model that is supposed to reflect the relation between high and low resolution. In this paper, we demonstrate that the reconstruction process is very sensitive to the actual relation between LR and HR images, and we argue that this is a substantial obstacle in deploying SRR in practice. We propose to search the hyper-parameter space using a genetic algorithm (GA), thus adapting to the actual relation between LR and HR, which has not been reported in the literature so far. The results of our extensive experimental study clearly indicate that our GA improves the capacities of SRR. Importantly, the GA converges to different values of the hyper-parameters depending on the applied degradation procedure, which is confirmed using statistical tests.


Genetic algorithm Image processing Super-resolution 



The reported work is a part of the SISPARE project run by Future Processing and funded by European Space Agency. The authors were partially supported by Institute of Informatics funds no. BK-230/RAu2/2017 (MK) and BKM-509/RAu2/2017 (DK).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Future ProcessingGliwicePoland
  2. 2.Silesian University of TechnologyGliwicePoland

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