| 苏冰山,吴炜,杨晓敏,李智,Gwanggil Jeon,陈雨.一种基于多传感器的红外图像正则化超分辨率算法[J].光电子激光,2015,26(2):368~377 |
| 一种基于多传感器的红外图像正则化超分辨率算法 |
| Infrared image regularization super-resolution algorithm based on multi-sensor |
| 投稿时间：2014-10-16 |
| DOI： |
| 中文关键词: 红外图像超分辨 多传感器 总广义变分(TGV)正则化 相位一致 |
| 英文关键词:infrared image super-resolution multi-sensor total generalized variation (TG V) regularization phase congruency |
| 基金项目:国家自然科学基金(61271330)、中国博士后科学基金(2014M552357)、四川省科技支撑计划(2014GZ0005)、 南京邮电大学 江苏省图像处理与图像通信重点实验室开放基金(LBEK2013001)、留学回国 人员科研启动基金和国家自然科学基金委员会与韩国国家研究基金会联合资助合作交流(6141101009)资助项目 (1.四川大学 电子信息学院,四川 成都,610065； 2.韩国仁川大学,仁川 402-749) |
| 作者 | 单位 | | 苏冰山 | 四川大学 电子信息学院,四川 成都,610065 | | 吴炜 | 四川大学 电子信息学院,四川 成都,610065 | | 杨晓敏 | 四川大学 电子信息学院,四川 成都,610065 | | 李智 | 四川大学 电子信息学院,四川 成都,610065 | | Gwanggil Jeon | 韩国仁川大学,仁 川 402-749 | | 陈雨 | 四川大学 电子信息学院,四川 成都,610065 |
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| 中文摘要: |
| 提出一种红外图像多传感器超分辨率重建算法。 算法存在两个关键点:一是有效利用两类图像的相关性；二是针对红外图像的特点利用其自 身信息 构造正则化模型。采用相位一致性算法提取可见光图像边缘,利用此边缘信息对正则化模型 加权,以 充分利用可见光和红外图像的相关性；将一阶梯度锐化算子引入总广义变分模型,构成针对 红外 图像特点的正则化模型；最后采用一阶主-对偶优化算法求得加权后模型的最优解。实验表 明,本文算法可获得边缘清晰的重建结果,并且有效抑制噪声,在主观视觉效果和客观评价 指标方面均优于其他算法。 |
| 英文摘要: |
| It′s necessary to improve the resolut ion of infrared image because the low-resolution image cannot meet the demand of many applications.Traditional a pproaches reconstruct infrared image merely from low-resolution infrared image,which deliver limited results. High-resolution visible image,by contrast,can be easily obtained with a CCD camera and has a strong correlation with the infrared image,from which we can increase the resolution of infrared image by utilizing the informat ion of visible image.This paper presents a new infrared image resolution improvement framework based on multi-s ensor.The proposed method consists of two key points.The first one is that the correlation between infrar ed and visible images should be used efficiently;the second one is that the regularization model should be suitable for infrared image super-resolution. We use phase congruency to extract the edges of visible image,and the edges are then combined with a regularization model,which utilizes the correlation sufficiently.In addition,the regularization model is built by first-order graduate operator and total generalized variation regularization,which is applic able to the reconstruction of infrared image.Finally,this method infers the super-resolved infrared image with a first -order primal-dual optimization scheme.Experimental results demonstrate that the proposed method can obtain clea r result s and suppress noise effectively. When compared with other methods,the proposed algorithm is superior in terms of subjective and objective qualities. |
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