Analysis of Different Image Enhancement and Feature Extraction Methods

Lucero Verónica Lozano-Vázquez, Jun Miura, Alberto Jorge Rosales-Silva, Alberto Luviano-Juárez and Dante Mújica-Vargas
Additional contact information
Lucero Verónica Lozano-Vázquez: Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional—ESIME Zacatenco, Mexico City 07738, Mexico
Jun Miura: LINCE Lab, Toyohashi University of Technology, Toyohashi 441-8580, Japan
Alberto Jorge Rosales-Silva: Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional—ESIME Zacatenco, Mexico City 07738, Mexico
Alberto Luviano-Juárez: Instituto Politécnico Nacional—UPIITA, Mexico City 07340, Mexico
Dante Mújica-Vargas: Department of Computer Science, Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Palmira, Cuernavaca 62490, Mexico

Mathematics, 2022, vol. 10, issue 14, 1-16

Abstract: This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable extraction of such features is difficult in poorly illuminated scenes. One promising approach is to apply an image enhancement method before feature extraction, which preserves the original characteristics of the scene. We thus propose to use the Multi-Scale Retinex algorithm, which is aimed to emulate the human visual system and it provides more information of a poorly illuminated scene. We experimentally assessed various combinations of image enhancement (MSR, Gamma correction, Histogram Equalization and Sharpening) and feature extraction methods (SIFT, SURF, ORB, AKAZE) using images of a large variety of scenes, demonstrating that the combination of the Multi-Scale Retinex and SIFT provides the best results in terms of the number of reliable feature matches.

Keywords: image enhancement; image feature extraction and matching; the Multi-Scale Retinex (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/14/2407/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/14/2407/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:14:p:2407-:d:859276

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().