Open AccessDigital image improvement by integrating mages of different resolutions
Author(s) -
Gabriel Scarmana
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/509/1/012046
Subject(s) - computer vision , artificial intelligence , computer science , zoom , pixel , image resolution , process (computing) , translation (biology) , digital image , sequence (biology) , image (mathematics) , computer graphics (images) , image processing , optics , biochemistry , chemistry , physics , messenger rna , gene , lens (geology) , operating system , genetics , biology
This proof of concept paper evaluates the performance of super-resolution (SR) imaging when combining a sequence of images of different resolutions. Traditional work in SR requires accurate sub-pixel registration and/or alignment techniques. The assumption is that all the frames in the sequence are captured with a random sub-pixel translation and at the same spatial resolution. However, if there is no relative motion between the scene and the sensor, the super-resolution problem can be approached by integrating images of different resolutions using the image re-sampling ratios as the enhancing factor. This may be the case of using digital zooms whereby a scene of interest is enhanced by integrating the information obtained from capturing said scene at different zoom levels. Preliminary results of conducting tests on synthetic and real data (scanned images) are presented. In both cases the process under-samples the image of an object of interest within a scene at different resolutions. By integrating these images using an algebraic process an improved composite is obtained containing more spatial information than that provided by simply interpolating on a single image.