IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS

Ana Carolina Correia Rézio, William Robson Schwartz, Helio Pedrini

2012

Abstract

There is currently a growing demand for high-resolution images and videos in several domains of knowledge, such as surveillance, remote sensing, medicine, industrial automation, microscopy, among others. High resolution images provide details that are important to tasks of analysis and visualization of data present in the images. However, due to the cost of high precision sensors and the limitations that exist for reducing the size of the image pixels in the sensor itself, high-resolution images have been acquired from super-resolution methods. This work proposes a method for super-resolving a sequence of images from the compensation residual learned by the features extracted in the residual image and the training set. The results are compared with some methods available in the literature. Quantitative and qualitative measures are used to compare the results obtained with super-resolution techniques considered in the experiments.

Paper Citation

in Harvard Style

Carolina Correia Rézio A., Robson Schwartz W. and Pedrini H. (2012). IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 135-144. DOI: 10.5220/0003861701350144

in Bibtex Style

@conference{visapp12,
author={Ana Carolina Correia Rézio and William Robson Schwartz and Helio Pedrini},
title={IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={135-144},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003861701350144},
isbn={978-989-8565-03-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS
SN - 978-989-8565-03-7
AU - Carolina Correia Rézio A.
AU - Robson Schwartz W.
AU - Pedrini H.
PY - 2012
SP - 135
EP - 144
DO - 10.5220/0003861701350144