IEICE TRANSACTIONS on Information
Retinex-Based Image Enhancement with Particle Swarm Optimization and Multi-Objective Function
Summary :
Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.12 pp.2721-2724
- Publication Date
- 2020/12/01
- Publicized
- 2020/09/15
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDL8085
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Farzin MATIN
Pukyong National University
Yoosoo JEONG
Daegu-Gyeongbuk Medical Innovation Foundation
Hanhoon PARK
Pukyong National University
Keyword
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Farzin MATIN, Yoosoo JEONG, Hanhoon PARK, "Retinex-Based Image Enhancement with Particle Swarm Optimization and Multi-Objective Function" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 12, pp. 2721-2724, December 2020, doi: 10.1587/transinf.2020EDL8085.
Abstract: Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8085/_p
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@ARTICLE{e103-d_12_2721,
author={Farzin MATIN, Yoosoo JEONG, Hanhoon PARK, },
journal={IEICE TRANSACTIONS on Information},
title={Retinex-Based Image Enhancement with Particle Swarm Optimization and Multi-Objective Function},
year={2020},
volume={E103-D},
number={12},
pages={2721-2724},
abstract={Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.},
keywords={},
doi={10.1587/transinf.2020EDL8085},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Retinex-Based Image Enhancement with Particle Swarm Optimization and Multi-Objective Function
T2 - IEICE TRANSACTIONS on Information
SP - 2721
EP - 2724
AU - Farzin MATIN
AU - Yoosoo JEONG
AU - Hanhoon PARK
PY - 2020
DO - 10.1587/transinf.2020EDL8085
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2020
AB - Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.
ER -
English
