夜间图像的白平衡算法研究与实现

陈光化; 张犁

Chinese

您当前的位置：

首页 >

文章列表页 >

夜间图像的白平衡算法研究与实现

更新时间：2026-09-04

- 夜间图像的白平衡算法研究与实现
- Research and Realization of White Balance Algorithm for Night Image
- 微电子学与计算机 2018年35卷第3期页码：33-36
- 作者机构：
  
  上海大学微电子研究与开发中心,上海,200072
- 作者简介：
- 基金信息：
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
移动端阅览
陈光化, 张犁. 夜间图像的白平衡算法研究与实现[J]. 微电子学与计算机, 2018, 35(3): 33-36, 41.

CHEN Guang-hua, ZHANG Li. Research and Realization of White Balance Algorithm for Night Image[J]. 2018, 35(3): 33-36.
陈光化, 张犁. 夜间图像的白平衡算法研究与实现[J]. 微电子学与计算机, 2018, 35(3): 33-36, 41. DOI：

CHEN Guang-hua, ZHANG Li. Research and Realization of White Balance Algorithm for Night Image[J]. 2018, 35(3): 33-36. DOI：

摘要

本文提出了一种基于像素点加权的白平衡算法

并且对算法进行了FPGA实现.该算法首先将RGB图像转化为暗通道图像

根据图像的暗通道值以及高光区域的分布情况为各像素点加入权重.然后

根据各像素点的权重计算出各通道的加权平均值

并将其结合标准灰度得到各通道的增益因子

最后利用增益因子完成白平衡调整

消除图像的颜色偏色.实验结果表明

该算法有效解决了夜间图像的偏色问题

并且满足了实时系统的要求.

Abstract

This paper a white balance algorithm based on the weight of pixels is proposed

and the algorithm is implemented by FPGA. Firstly

the RGB image is converted into a dark channel image

and the weight of pixels is achieved according to the dark channel value of the image and the distribution of the high light region. Then

the weighted mean of each channel is calculated according to the weight of each pixel

and the gain factor of each channel is obtained by combining the standard gray scale. Finally

the gain factor is used to adjust the white balance to eliminate the color deviation of the image. The experimental results show that the algorithm can effectively solve the problem of color cast of nighttime image and meet the requirements of real-time system.

关键词

Keywords

references

Sikudová E. A gamut-mapping framework for color-accurate reproduction of HDR images[J]. IEEE Computer Graphics and Applications, 2016, 36(4): 78-90. DOI: 78

Prayitno A, Wibawa A D, Purnomo M H. Early detection study of kidney organ complication caused by diabetes mellitus using iris image color constancy[C]//2016 International Conference on Information % Communication Technology and Systems (ICTS). Surabaya, Indonesia, 2016: 146-149.

Kuzhaloli S, Premi M S G, Bernatin T, et al. Insitu implementation of adaptive color constancy algorithm[C]//2016 Online International Conference on Green Engineering and Technologies (IC-GET). Coimbatore, India, 2016: 1-4.

Banic N, Loncaric S. Improving the white patch method by subsampling[C]//Image Processing (ICIP), 2014 IEEE International Conference on. Paris, IEEE, 2014: 605 - 609.

Seonhee Park, Byeongho Moon, Seungyong Ko, et al. Low-light image enhancement using variational optimization-based Retinex model[C]//2017 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, 2017: 70-71.

FrÖhlich J, Schilling A, Eberhardt B. Gamut mapping for digital cinema[J]. SMPTE Motion Imaging Journal, 2014, 123(8): 41-48. DOI: 41

Hui Z, Sankaranarayanan A C, Sunkavalli K, et al. White balance under mixed illumination using flash photography[C]//2016 IEEE International Conference on Computational Photography (ICCP). Evanston, 2016: 1-10.

Sethi R, Sreedevi I, Verma O P, et al. An optimal underwater image enhancement based on fuzzy gray world algorithm and Bacterial Foraging algorithm[C]//2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). Patna, 2015: 1-4.

Azimi M, Bronner T F, Boitard R, et al. A hybrid approach for efficient color gamut mapping[C]//2017 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, 2017: 254-255.

Yanlin Qian, Chen K, Kämäräinen J K, et al. Deep structured-output regression learning for computational color constancy[C]//2016 23rd International Conference on Pattern Recognition (ICPR). Cancun, Mexico, 2016: 1899-1904.

West G, Brill M H. Necessary and sufficient conditions for Von Kries chromatic adaptation to give color constancy[J]. Journal of Mathematical Biology, 1982, 15(2): 249-258. DOI: 249

Fourure D, Emonet R, Fromont E, et al. Mixed pooling neural networks for color constancy[C]//2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, 2016: 3997-4001.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

一种基于共享缓存的星载数据交换架构设计

5G定位算法的FPGA加速设计研究

YOLO目标检测后处理算法的优化和硬件加速

基于FPGA的H.264可变块运动估计算法的优化及实现

基于TRNG与PUF集成结构的安全加密认证系统