Open AccessSurvey on CNN based super resolution methods
Author(s) -
Rafaa Amen Kazem,
Jamila H. Suad,
Huda Abdulaali Abdulbaqi
Publication year - 2021
Publication title -
journal la multiapp
Language(s) - English
Resource type - Journals
eISSN - 2721-1290
pISSN - 2716-3865
DOI - 10.37899/journallamultiapp.v2i4.444
Subject(s) - computer science , convolutional neural network , artificial intelligence , resolution (logic) , computer vision , boosting (machine learning) , sub pixel resolution , pixel , image resolution , superresolution , field (mathematics) , low resolution , high resolution , pattern recognition (psychology) , image (mathematics) , image processing , remote sensing , digital image processing , geography , mathematics , pure mathematics
Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.