文章摘要
王晓红,翟焱修,麻祥才.基于NSST多尺度自适应的Retinex低照度图像增强算法[J].包装工程,2020,41(3):211-217.
WANG Xiao-hong,ZHAI Yan-xiu,MA Xiang-cai.Retinex Low Light Image Enhancement Algorithm Based on Multi-Scale Adaptive NSST[J].Packaging Engineering,2020,41(3):211-217.
基于NSST多尺度自适应的Retinex低照度图像增强算法
Retinex Low Light Image Enhancement Algorithm Based on Multi-Scale Adaptive NSST
投稿时间:2019-10-16  修订日期:2020-02-10
DOI:10.19554/j.cnki.1001-3563.2020.03.033
中文关键词: 低照度图像增强  Retinex  自适应NSST
英文关键词: low light image enhancement  Retinex  adaptive NSST
基金项目:上海市出版印刷高等专科学校柔板印刷绿色制版与标准化实验室资助项目(ZBKT201809);上海市教育发展基金会和上海市教育委员会“晨光计划”(18CGB09)
作者单位
王晓红 1.上海理工大学上海 200093 
翟焱修 1.上海理工大学上海 200093 
麻祥才 2.上海出版印刷高等专科学校上海 200093 
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中文摘要:
      目的 在对低照度图像进行增强时,针对传统频率域方法由于尺度不够丰富而不能很好保留图像高频细节的问题,提出一种基于NSST多尺度自适应的Retinex低照度图像增强算法。方法 首先将低照度图像转化至HSI颜色空间后,单独对I通道进行处理,实现对图像色彩信息的保真效果;然后对I通道进行Retinex算法得到反射分量,从而去除照度信息对图像亮度的影响;对反射分量进行伽马调整后,进行基于La(平均亮度)、Pa(平均对比度)、Ia(信息熵)等3个特征值的自适应NSST分解,从而得到最佳参数的高频分量。结果 在主观观察和客观无参考图像质量评价中,文中算法的增强效果和评价得分都要优于其他算法。结论 经过自适应参数优化之后,低照度图像的对比度得到了提高,可视性和图像质量都得到了显著提升。
英文摘要:
      The work aims to propose the Retinex low light image enhancement algorithm based on multi-scale adaptive NSST, regarding the problem that the traditional frequency domain method cannot properly keep the high-frequency details of the image due to the insufficient scale in low light image enhancement. Firstly, after the low light image was transformed into HSI color space, I channel was processed separately to achieve the fidelity effect of image color information. Then, the reflection component of I channel was obtained by Retinex algorithm, so as to remove the influence of illuminance component on image brightness. After gamma adjustment of the reflection component, based on La (average brightness), Pa (average contrast) and Ia (information entropy), the best high frequency component was obtained by the adaptive NSST decomposition. In subjective observation and objective non reference image quality evaluation, the enhancement effect and evaluation score of the proposed algorithm were better than other algorithms. After the optimization of adaptive parameters, the contrast of low light image is improved, and the visibility and image quality are improved.
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