Authors:
Michal Kawulok
1
;
Daniel Kostrzewa
1
;
Pawel Benecki
1
and
Lukasz Skonieczny
2
Affiliations:
1
Future Processing and Silesian University of Technology, Poland
;
2
Future Processing, Poland
Keyword(s):
Genetic Algorithm, Image Processing, Super-resolution Reconstruction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Enterprise Information Systems
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Vision and Perception
Abstract:
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 a
rgue 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.
(More)