已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Semidecoupled decomposition-based fractional-order variational model for low-light enhancement

颜色恒定性 人工智能 计算机科学 计算机视觉 能见度 图像质量 图像增强 直方图 图像(数学) 过程(计算) 模式识别(心理学) 光学 物理 操作系统
作者
Bao Chen,Xiaohua Ding,Boying Wu
出处
期刊:Journal of Electronic Imaging [SPIE]
卷期号:31 (06)
标识
DOI:10.1117/1.jei.31.6.063002
摘要

Low-light enhancement is an important technique for improving image quality. This is because low-light enhancement is expected to improve image visibility while maintaining visual naturalness of the image. In recent years, many methods have been researched to enhance low-light images, including histogram-based, fusion-based, and learning-based methods. The most representative and widely used low-light image enhancement method is the so-called Retinex-based method. However, they tend to have many limitations. The limitations of the Retinex-based method are as follows. (1) Due to strong imaging noise or less-effective image decomposition, this results in a large number of artifacts in the enhanced results. (2) Although the first problem can be partially solved by exploring prior information, it often complicates the optimization process. (3) Small-magnitude details are often lost in enhanced results. To overcome these drawbacks, we propose a model called the fractional-order Retinex model. At the same time, Retinex images are decomposed in an effective semidecoupled way. More concretely, the illumination layer T is gradually estimated only with the observed image S based on the proposed variation model, whereas the reflectance layer R is jointly estimated by the intermediate T and S. Experimental results demonstrate the effectiveness of our method.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
万能图书馆应助dapis采纳,获得10
1秒前
2秒前
3秒前
恋雅颖月应助miles采纳,获得10
4秒前
4秒前
岂曰无衣完成签到 ,获得积分10
4秒前
大模型应助victoria采纳,获得10
6秒前
大头发布了新的文献求助10
8秒前
8秒前
赵杰发布了新的文献求助10
9秒前
10秒前
予秋发布了新的文献求助10
10秒前
momo发布了新的文献求助10
10秒前
我是老大应助瘦瘦语蕊采纳,获得10
14秒前
大面包发布了新的文献求助10
14秒前
16秒前
QTQ发布了新的文献求助10
16秒前
17秒前
怕黑大白菜真实的钥匙完成签到,获得积分10
17秒前
EMM完成签到 ,获得积分10
19秒前
19秒前
21秒前
victoria发布了新的文献求助10
22秒前
22秒前
不呐呐发布了新的文献求助10
23秒前
whatever应助喏晨采纳,获得10
23秒前
瓦学弟的妈妈完成签到 ,获得积分20
24秒前
负责怀莲发布了新的文献求助10
24秒前
25秒前
温柔烧鹅完成签到,获得积分10
25秒前
27秒前
Thea发布了新的文献求助30
28秒前
温柔烧鹅发布了新的文献求助10
28秒前
29秒前
瘦瘦语蕊发布了新的文献求助10
29秒前
31秒前
为医消得人憔悴完成签到 ,获得积分10
31秒前
zjgjnu发布了新的文献求助10
32秒前
32秒前
33秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989857
求助须知:如何正确求助?哪些是违规求助? 3531994
关于积分的说明 11255679
捐赠科研通 3270758
什么是DOI,文献DOI怎么找? 1805053
邀请新用户注册赠送积分活动 882195
科研通“疑难数据库(出版商)”最低求助积分说明 809208