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首页夜间视觉增强:低光可见光与红外图像融合方法
"Fusion of the low-light-level visible and infrared images for night-vision context enhancement" 是一项针对夜间视觉应用的研究,旨在通过融合低光照水平的可见光和红外图像来提升夜视环境下的视觉效果。 在夜视应用中,低光照条件下的可见光和红外成像技术具有重要的作用。本文提出的FNCE(Fusion for Night-Vision Context Enhancement)方法旨在解决这一问题。首先,研究中引入了一种自适应亮度拉伸方法,该方法针对可见光图像进行处理,以增强图像的亮度和对比度,尤其在低光照环境下,能够更好地揭示图像细节。 接下来,为了进一步提升图像的质量,研究采用了混合多尺度分解与边缘保护过滤相结合的技术。这种分解方法可以在保留图像边缘清晰度的同时,对源图像进行多尺度分析,从而提取出不同层次的特征信息。多尺度分解有助于识别图像中的各种结构和纹理,而边缘保护过滤则确保了在处理过程中图像轮廓的完整性。 最后,通过三种不同的融合规则,将分解后的图像进行合成,以获得最终的融合结果。这一步骤的关键在于如何有效地结合不同尺度的信息,以达到最佳的视觉效果。实验结果显示,FNCE方法在细节(边缘)、对比度、锐度以及人眼视觉感知方面都表现出优于传统方法的性能。因此,该方法对于提升夜间视觉环境下的图像质量和用户体验具有显著的效果。 此研究的OCIS代码为350.2660,表明它属于光学领域的特定子领域。FNCE方法为夜视成像提供了一种创新的解决方案,对于改善低光照条件下的图像处理和视觉感知具有重要的实际应用价值。
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Fusion of the low-light-level visible and infrared images
for night-vision context enhancement
Jin Zhu (朱 进), Weiqi Jin (金伟其)*, Li Li (李 力), Zhenghao Han (韩正昊),
and Xia Wang (王 霞)
School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
*Corresponding author: jinwq@bit.edu.cn
Received October 29, 2017; accepted November 10, 2017; posted online December 5, 2017
For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for
night-vision context enhancement (FNCE) method is proposed. An adaptive brightness stretching method is
first proposed for enhancing the visible image. Then, a hybrid multi-scale decomposition with edge-preserving
filtering is proposed to decompose the source images. Finally, the fused result is obtained via a combination of the
decomposed images in three different rules. Experimental results demonstrate that the FNCE method has better
performance on the details (edges), the contrast, the sharpness, and the human visual perception. Therefore,
better results for the night-vision context enhancement can be achieved.
OCIS codes: 350.2660, 040.3780, 100.2980, 110.3080.
doi: 10.3788/COL201816.013501.
The low-light-level visib le images always provide the
details and background scenery, while the target is often
detected/recognized via the infrared imaging
[1,2]
in night
vision. As the visible and infrared image fusion technology
can improve the perception of the scene in addition to the
ability to detect/recognize the target
[3]
, the fusion technol-
ogy of the low-light-level visible and infrared images plays
a significant role in night vision and has been successfully
applied in the areas of defense and security
[4]
.
However, night-vision images usually have relatively
strong noise, low contrast, and unclear details (including
edges). Moreover, human eyes are very sensitive to the de-
tails and noise. As these factors have not been considered
in most proposed fusion meth ods, it is difficult to achieve
good results in night vision. Thus, an appropriate fusion
technology is required for night vision to obtain better re-
sults for the night-vision context enhancement.
Liu et al. proposed a modified method to fuse the visible
and infrared images for night vision
[5]
. In the method, the
visible image is enhanced via the corresponding infrared
image, and the fused result is obtained using a conven-
tional multi-scale fusion method. The details of the visible
image are not fully enhanced. Salient targets in the infra-
red image are displayed in dark pixels, which is not good
for visual perception. A fusion method for low-light visible
and infrared images based on contourlet transform is
proposed
[6]
. Different rules are used for the combination
of the low-frequency and high-frequency information.
The details of the visible image are not fully enhanced
either. Zhou et al. proposed a guided-filter-based context
enhancement (GFCE) fusion method for night vision
[7]
.
In the result of the GFCE method, the noise has been
amplified along with the detail enhancement, and some
distortions may emerge in the bright regions due to
over enhancement. In all of these methods, neither a
denoising method nor a detail enhancing method is used.
Furthermore, the details (including edges) cannot be
preserved well enough during the fusion process. Thus,
further research needs to be done to obtain better
context-enhancement results for the low-light-level visible
and infrared imaging.
In order to address the above problems for better night-
vision applications, a low-light-level visible and infrared
images fusion framework for night-vision context enhance-
ment (FNCE) is proposed in this Letter, as shown in Fig.
1.
Actually, the FNCE method can be divided into two
parts: the initial enhancement and the fusion process.
In the initial enhancement, an adaptive brightness
stretching method has been first proposed to enhance
the visibility of the low-light-level visible image. At the
same time, the denoising and detail enhancement methods
are applied for source images. As the multi-scale
Fig. 1. The proposed infrared and visible image FNCE.
COL 16(1), 013501(2018) CHINESE OPTICS LETTERS January 10, 2018
1671-7694/2018/013501(6) 013501-1 © 2018 Chinese Optics Letters
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