期刊文献+

基于自适应多特征融合的红外图像增强算法

Infrared Image Enhancement Algorithm Based on Adaptive Multi-Feature Fusion
下载PDF
导出
摘要 针对红外图像纹理不清晰、亮度低、高噪声的问题,提出了一种自适应多特征融合的红外图像增强算法。首先,通过用自动线性映射的方法对14位红外图像进行有效特征提取得到了16位图像,提升了图像可视化效果。其次,引入广义反锐化掩模(Generalized Unsharp Masking,GUM)算法与带色彩恢复的多尺度视网膜(Multi-Scale Retinex with Color Restoration,MSRCR)增强算法联合处理的方法,获得图像不同尺度的有效信息,提升了图像的对比度。最后设计了自适应权重图,并结合图像金字塔结构的特性,对不同特征层进行有效信息的互补融合,提升了图像亮度,丰富了图像的纹理信息。实验结果表明,此算法有效提升了红外图像的对比度和视觉效果;相较于现有的几种算法,其平均梯度(Average Gradient,AG)约提升0.6%,峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)约提升10%,图像的边缘信息有效率约提升11%,图像的清晰度约提升10%。 Aiming at the problems of unclear texture,low brightness and high noise of infrared images,an adaptive multi-feature fusion algorithm for infrared image enhancement is proposed in this paper.Firstly,the automatic linear mapping method is used to extract 16-bit infrared images from 14-bit infrared images,which improves the visual effect.Secondly,the combined processing method of GUM and MSRCR is introduced to obtain effective information on different scales of the image and improve the contrast of the image.Finally,the adaptive weight map is designed and combined with the characteristics of the image pyramid structure to complement and fuse the effective information of different feature layers,which improves the brightness of the image and enriches the texture information of the image.Experimental results show that this algorithm can effectively improve the contrast and visual effect of infrared images,and its AG is increased by about 0.6% compared with the existing algorithms.The PSNR is about 10% higher,the image edge information efficiency is about 11% higher,and the image sharpness is about 10% higher.
作者 邸若海 万乐乐 李亮亮 孙梦宇 李晓艳 王鹏 DI Ruo-hai;WAN Le-le;LI Liang-liang;SUN Meng-yu;LI Xiao-yan;WANG Peng(College of Electronic Information Engineering,Xi'an Technological University,Xi'an 710021,China;College of Mechanical Engineering,Xi'an Technological University,Xi'an 710021,China;College of Optoelectronic Engineering,Xi'an Technological University,Xi'an 710021,China)
出处 《红外》 CAS 2024年第7期16-28,共13页 Infrared
基金 国家自然科学基金项目(621713600) 陕西省科技厅重点研发计划项目(2022GY-110) 国家重点研发计划(2022YFF0604900) 2022年度陕西高校青年创新团队项目 山东省智慧交通重点实验室(筹)项目 2023年陕西省高校工程研究中心项目。
关键词 特征提取 权重图 金字塔 多尺度融合 feature extraction weight map pyramid multiscale fusion
  • 相关文献

参考文献6

二级参考文献76

  • 1王炳健,刘上乾,周慧鑫,李庆.基于平台直方图的红外图像自适应增强算法[J].光子学报,2005,34(2):299-301. 被引量:101
  • 2Mooney J M. Ilf noise measurement on PtSi focal plane arrays[ C ]//Proceedings qISPIE, 1990, 1308:122-131.
  • 3Rogalski A. Infrared detectors[J]. An overview, Infrored Physics & Technology. 2002, 43:187-210.
  • 4Olivier R1OU, Stephane BERREBI and Pierre BREMOND. Non Unifon'nity Correction and themlal drift compensation of thermal infrared camera[C]//Proc'eedings of SPIE, 2004, 5405:294-302.
  • 5Scribner D A , Sarkady K A , Kruer M R, et al. Adaptive retina-like preprocessing for imaging detector arrays[C]//Proceedings of IEEE lnternutional Conferem'e in Neural Networks, 1993. 1953:1955-1960.
  • 6Zuo C, Chen Q, Gu G H, et al. New temporal high-pass filter nonuniformity correction based on bilateral filter[J]. Opt. Rev, 2011 (18): 197- 202.
  • 7Harris J G, Yu-Ming C. Nonuniformity correction of infrared image sequences using the constant-statistics constraint[J]. Image Process IEEE Trans. 1999(8): 1148-1151.
  • 8Zuo C, Chen Q, Gu G H, et al. Scene-based nonunitbrmity correction method using multiscale constant statistics[J]. Opt. Eng. 2011, 50(8): 087006.
  • 9Rossi A, Diani M, Corsini G. Temporal statistics de-ghosting for adaptive nonunifonnity correction in infrared focal plane arrays[J] Electron. Lett, 2010, 46:348-U4869.
  • 10Scribner D A, Sarkady K A, Kruer M R, et al. Adaptive nonuniformity correction for IR focal-plane arrays using neura networks[C]//Proceedings ofSPIE, T.S.J. Jayadev (Ed): 1991, 1541 100-109.

共引文献109

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部