海面红外图像的动态范围压缩及细节增强
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摘要
动态范围压缩和细节增强是红外图像处理的两个重要课题。为了将高动态海面背景红外图像清晰显示,提出一种高动态范围压缩及细节增强算法。首先,通过基于梯度边缘信息的多方向拉普拉斯增强方法,将梯度图像平滑处理,并与多方向拉普拉斯滤波相乘,实现高动态范围图像的细节增强;然后统计增强后图像的动态广义直方图信息;最后采用灰度级分组的方法构造映射函数,将高动态范围压缩到8 bits,输出可清晰显示的红外图像。对大量海面背景红外图像进行实验分析,结果表明,该算法提高了图像的对比度,有效增强了舰船目标细节,同时抑制了海面背景噪声的放大和光晕现象的产生,最终获得较好的输出图像。
Dynamic range compression and detail enhancement are two important issues for effectively displaying high dynamic range infrared(IR) images on standard dynamic range monitors. Sophisticated techniques are required in order to improve the visibility of the details without introducing distortions. To clearly show the highly dynamic infrared image of sea background, a high dynamic range compression and detail enhancement algorithm was presented which was made of three main steps. First, highly dynamic infrared image was enhanced by a multi-directional Laplacian enhancement method based on gradient edge information, and the first order gradient image smoothed was combined with multi-directional Laplacian filtering to enhance images. Second, the dynamic generalized histogram information of the enhanced image was obtained. Finally, the gray level grouping method was used to construct the mapping function, which maps 14 bits intensity in the input image to 8 bits intensity in the output image.As a result, infrared image with low dynamic range was obtained clearly. Experiments on a large number of sea-surface infrared images were conducted. The results verify that the proposed algorithm can improve the contrast of the image, effectively enhance the ship target details, suppress background noise amplification, and avoid the generation of halos. Therefore, the image with high quality was achieved.
Dynamic range compression and detail enhancement are two important issues for effectively displaying high dynamic range infrared(IR) images on standard dynamic range monitors. Sophisticated techniques are required in order to improve the visibility of the details without introducing distortions. To clearly show the highly dynamic infrared image of sea background, a high dynamic range compression and detail enhancement algorithm was presented which was made of three main steps. First, highly dynamic infrared image was enhanced by a multi-directional Laplacian enhancement method based on gradient edge information, and the first order gradient image smoothed was combined with multi-directional Laplacian filtering to enhance images. Second, the dynamic generalized histogram information of the enhanced image was obtained. Finally, the gray level grouping method was used to construct the mapping function, which maps 14 bits intensity in the input image to 8 bits intensity in the output image.As a result, infrared image with low dynamic range was obtained clearly. Experiments on a large number of sea-surface infrared images were conducted. The results verify that the proposed algorithm can improve the contrast of the image, effectively enhance the ship target details, suppress background noise amplification, and avoid the generation of halos. Therefore, the image with high quality was achieved.
引文
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[13] A Onur Karali, O Erman Okman, Aytai T. Adaptive enhancement of sea-surface targets in infrared images based on local frequency cues[J]. Journal of the Optical Society of America A Optics Image Science&Vision, 2010, 27(3):509-517.
[14] Garcia F. Real-time visualization of low contrast targets from high-dynamic range infrared images based on temporal digital detail enhancement filter[J]. Journal of Electronic Imaging, 2015, 24(6):061103.
[15] Zhang F, Xie W, Ma G, et al. High dynamic range compression and detail enhancement of infrared images in the gradient domain[J]. Infrared Physics&Technology,2014, 67:441-454.
[16] Gonzalez R C, Woods R E. Digital Image Processing[M].Translated by Ruan Qiuqi, et al. Beijing:Publishing House of Electronics Industry, 2010.(in Chinese)
[17] Yoon B W, Song W J. Image contrast enhancement based on the generalized histogram[J]. Journal of Electronic Imaging,2007, 16(3):033005.
[18] Chen Z, Abidi B R, Page D L, et al. Gray-level grouping(GLG):an automatic method for optimized image contrast enhancement-Part I:the basic method[J]. IEEE Transactions on Image Processing, 2006, 15(8):2290-2302.
[19] Celik T, Tjahjadi T. Contextual and Variational Contrast Enhancement[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2011,20(12):3431.
[2] Zhou Qiang, Zhao Jufeng, Feng Huajun, et al. Infrared image en-hancement using polarization imaging[J]. Infrared and Laser Engineering, 2014, 43(1):39-47.(in Chinese)周强,赵巨峰,冯华君,等.基于偏振成像的红外图像增强[J].红外与激光工程, 2014, 43(1):39-47.
[3] Lai Y R, Tsai P C, Yao C Y, et al. Improved local histogram equalization with gradient-based weighting process for edge preservation[J]. Multimedia Tools&Applications,2017, 76(1):1-29.
[4] Wang Y, Pan Z. Image contrast enhancement using adjacent-blocks-based modification for local histogram equalization[J]. Infrared Physics&Technology, 2017, 86:59-65.
[5] Chen Boyang. Local linear enhancement of luminance histogram of color remote sensing image.[J]. Optics and Precision Engineering, 2017, 25(2):502-508.(in Chinese)陈博洋.彩色遥感图像的亮度直方图局部线性化增强[J].光学精密工程, 2017, 25(2):502-508.
[6] Branchitta F, Porta A. Dynamic-range compression and contrast enhancement in infrared imaging systems[C]//SPIE,2008, 6737(7):076401.
[7] Zuo C, Chen Q, Ren J. Display and detail enhancement for high-dynamic-range infrared images[J]. Optical Engineering,2011, 50(12):895-900.
[8] Hao Zhicheng, Wu Chuan, Yang Hang, et al. Image detail enhancement method based on multi-scale bilateral texture filter[J]. Chinese Optics, 2016, 9(4):423-431.(in Chinese)郝志成,吴川,杨航,等.基于双边纹理滤波的图像细节增强方法[J].中国光学, 2016, 9(4):423-431.
[9] Xu Honglie, Chen Qian, Gu Guohua, et al. High dynamic range image enhancement technology based on guided image filter[J]. Infrared and Laser Engineering, 2015, 44(12):3843-3849.
[10] Jia Hongguang, Wu Zepeng, Zhu Mingchao, et al. Infrared image enhancement based on generalized linear operation and bilateral filter[J]. Optics and Precision Engineering, 2013,21(12):3272-3282.(in Chinese)贾宏光,吴泽鹏,朱明超,等.基于广义线性运算和双边滤波的红外图像增强[J].光学精密工程, 2013, 21(12):3272-3282.
[11] Branchitta F, Diani M, Romagnoli M. New technique for the visualization of high dynamic range infrared images[J].Optical Engineering, 2009, 48(9):6401.
[12] Rossi A, Acito N, Diani M. Dynamic range reduction and contrast adjustment of infrared images in surveillance scenarios[J]. Optical Engineering, 2013, 52(10):102002.
[13] A Onur Karali, O Erman Okman, Aytai T. Adaptive enhancement of sea-surface targets in infrared images based on local frequency cues[J]. Journal of the Optical Society of America A Optics Image Science&Vision, 2010, 27(3):509-517.
[14] Garcia F. Real-time visualization of low contrast targets from high-dynamic range infrared images based on temporal digital detail enhancement filter[J]. Journal of Electronic Imaging, 2015, 24(6):061103.
[15] Zhang F, Xie W, Ma G, et al. High dynamic range compression and detail enhancement of infrared images in the gradient domain[J]. Infrared Physics&Technology,2014, 67:441-454.
[16] Gonzalez R C, Woods R E. Digital Image Processing[M].Translated by Ruan Qiuqi, et al. Beijing:Publishing House of Electronics Industry, 2010.(in Chinese)
[17] Yoon B W, Song W J. Image contrast enhancement based on the generalized histogram[J]. Journal of Electronic Imaging,2007, 16(3):033005.
[18] Chen Z, Abidi B R, Page D L, et al. Gray-level grouping(GLG):an automatic method for optimized image contrast enhancement-Part I:the basic method[J]. IEEE Transactions on Image Processing, 2006, 15(8):2290-2302.
[19] Celik T, Tjahjadi T. Contextual and Variational Contrast Enhancement[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2011,20(12):3431.