TY - JOUR
T1 - Analysis of Different Image Enhancement and Feature Extraction Methods
AU - Lozano-Vázquez, Lucero Verónica
AU - Miura, Jun
AU - Rosales-Silva, Alberto Jorge
AU - Luviano-Juárez, Alberto
AU - Mújica-Vargas, Dante
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - image enhancement
KW - image feature extraction and matching
KW - the Multi-Scale Retinex
UR - http://www.scopus.com/inward/record.url?scp=85136176148&partnerID=8YFLogxK
U2 - 10.3390/math10142407
DO - 10.3390/math10142407
M3 - Artículo
AN - SCOPUS:85136176148
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 14
M1 - 2407
ER -