利用色彩校正和对比度技术增强水下图像

Vijay Kumar Gowda B N, Sabitha Gauni, V. Maik
{"title":"利用色彩校正和对比度技术增强水下图像","authors":"Vijay Kumar Gowda B N, Sabitha Gauni, V. Maik","doi":"10.1109/ICIIP53038.2021.9702650","DOIUrl":null,"url":null,"abstract":"An image in the underwater ocean depth of 10 meters by capturing a low-resolution camera. An image is characterized by low contrast and blurriness. Despite water medium has considering the propagation of light, it degrades the underwater image due to refraction, scatters, and color absorption. This underwater image needs to improve its color contrast and quality image by using Simple Histogram Equalization for color correction and DSNMF (Deep Sparse Non-Negative Matrix Factorization) for color contrast improvement. Finally, the proposed method results describe based on the observations of the qualitative and quantitative parameters of PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), and SSIM (System Similarity Index Matrix). The output of This proposed method is shown a qualitative state-of-the-art underwater enhanced image with better brightness and contrast and developed technique simulation produces a quantitative output of enhancing the image of PSNR, RMSE, and SSIM are 25.256, 13.235, and 8.232, with these parameters increasing better quality visual perception.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Underwater image enhancement by using color correction and contrast techniques\",\"authors\":\"Vijay Kumar Gowda B N, Sabitha Gauni, V. Maik\",\"doi\":\"10.1109/ICIIP53038.2021.9702650\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An image in the underwater ocean depth of 10 meters by capturing a low-resolution camera. An image is characterized by low contrast and blurriness. Despite water medium has considering the propagation of light, it degrades the underwater image due to refraction, scatters, and color absorption. This underwater image needs to improve its color contrast and quality image by using Simple Histogram Equalization for color correction and DSNMF (Deep Sparse Non-Negative Matrix Factorization) for color contrast improvement. Finally, the proposed method results describe based on the observations of the qualitative and quantitative parameters of PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), and SSIM (System Similarity Index Matrix). The output of This proposed method is shown a qualitative state-of-the-art underwater enhanced image with better brightness and contrast and developed technique simulation produces a quantitative output of enhancing the image of PSNR, RMSE, and SSIM are 25.256, 13.235, and 8.232, with these parameters increasing better quality visual perception.\",\"PeriodicalId\":431272,\"journal\":{\"name\":\"2021 Sixth International Conference on Image Information Processing (ICIIP)\",\"volume\":\"321 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Sixth International Conference on Image Information Processing (ICIIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIIP53038.2021.9702650\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Sixth International Conference on Image Information Processing (ICIIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIIP53038.2021.9702650","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

这张图片是由低分辨率相机在水下10米深处拍摄的。图像的特点是低对比度和模糊。尽管水介质考虑了光的传播,但由于水介质的折射、散射和色彩吸收等因素,使水下图像质量下降。该水下图像需要通过简单直方图均衡化(Simple Histogram Equalization)进行色彩校正,DSNMF (Deep Sparse Non-Negative Matrix Factorization)进行色彩对比度提升,提高图像质量。最后,基于对峰值信噪比(PSNR)、均方根误差(RMSE)和系统相似度指数矩阵(SSIM)等定性和定量参数的观测,对所提方法的结果进行了描述。本文提出的方法输出的水下增强图像具有较好的亮度和对比度,并且开发的技术仿真得到了定量输出的增强图像,其中PSNR、RMSE和SSIM分别为25.256、13.235和8.232,这些参数增加了较好的视觉感知质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Underwater image enhancement by using color correction and contrast techniques
An image in the underwater ocean depth of 10 meters by capturing a low-resolution camera. An image is characterized by low contrast and blurriness. Despite water medium has considering the propagation of light, it degrades the underwater image due to refraction, scatters, and color absorption. This underwater image needs to improve its color contrast and quality image by using Simple Histogram Equalization for color correction and DSNMF (Deep Sparse Non-Negative Matrix Factorization) for color contrast improvement. Finally, the proposed method results describe based on the observations of the qualitative and quantitative parameters of PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), and SSIM (System Similarity Index Matrix). The output of This proposed method is shown a qualitative state-of-the-art underwater enhanced image with better brightness and contrast and developed technique simulation produces a quantitative output of enhancing the image of PSNR, RMSE, and SSIM are 25.256, 13.235, and 8.232, with these parameters increasing better quality visual perception.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Weather Station Using Raspberry Pi Prediction of Heart Disease using Machine Learning Techniques A Brief Review on Existing Techniques for Detecting Digital Image Forgery A Novel Approach for Excavating Communication Using Taxonomy and Outline Mechanisms A Novel Rough Set Based Image Denoising Algorithm
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1