浏览全部资源
扫码关注微信
收稿：2021-02-08，
修回：2021-6-3，
录用：2021-6-10，
纸质出版：2022-05-16
移动端阅览
目的
2
曝光融合算法，即将多幅不同曝光时间的图像融合得到一幅曝光度良好的图像，可能在最终的输出图像中引入光晕伪影、边缘模糊和细节丢失等问题。针对曝光融合过程中存在的上述问题，本文从细节增强原理出发提出了一种全细节增强的曝光融合算法。
方法
2
在分析了光晕现象产生原因的基础上，从聚合的新角度对经典引导滤波进行改进，明显改善引导滤波器的保边特性，从而有效去除或减小光晕；用该改进引导滤波器提取不同曝光图像的细节信息，并依据曝光良好度将多幅细节图融合得到拍摄场景的全细节信息；将提取、融合得到的全细节信息整合到由经典曝光融合算法得到的初步融合图像上，最终输出一幅全细节增强后的融合图像。
结果
2
实验选取17组多曝光高质量图像作为输入图像序列，本文算法相较于其他算法得到的融合图像边缘保持较好，融合自然；从客观指标看，本文算法在信息熵、互信息与平均梯度等指标上都较其他融合算法有所提升。以本文17组图像的平均结果来看，本文算法相较于经典的拉普拉斯金字塔融合算法在信息熵上提升了14.13%，在互信息熵上提升了0.03%，在平均梯度上提升了16.45%。
结论
2
提出的全细节增强的曝光融合算法将加权聚合引导滤波用于计算多曝光序列图像的细节信息，并将该细节信息融合到经典曝光融合算法所得到的一幅中间图像之上，从而得到最终的融合图像。本文的处理方法使最终融合图像包含更多细节，降低或避免了光晕及梯度翻转等现象，且最终输出图像的视觉效果更加优秀。
Objective
2
Image quality is constrained of over-exposure or under-exposure due to the higher dynamic range is much than image capture/display devices in real scene. There is two problem-solving as mentioned below: The first one is to obtain high dynamic range (HDR) images using high dynamic imaging devices. Then
the tone mapping algorithm is used to compress the HDR images to deduct dynamic range (LDR) images for displaying on LDR display devices; The second method
which is named as exposure fusion
fuses multi-exposure levels images to obtain the LDR image straight forward. Compared to the first one
the second exposure fusion method is not costly subjected to HDR capture devices and has its lower computational complexity. However
exposure fusion techniques may introduce some artifacts (e.g.
halos
edges blurring and details losing) in final generated images.
Method
2
Our two crucial tasks facilitate these issues and improve the performance of the exposure fusion. First
an improved guided image filtering
which is referred to as weighted aggregating guided image filtering (WAGIF)
demonstrated and extracted fine details based on multiple exposed images. The employed average aggregation is indiscriminate for all patches in guided image filtering (GIF)
in some of which the filtering output of a pixel located on the edges is close to the mean which is far beyond the expected value
and yields blurring edges. A novel weighted aggregation strategy for GIF is issued to improve the edge-preserving performance. Different from the average aggregation related to the original GIF
multi-windows predictions of the interest pixel are aggregated via weighting instead of averaged value. Moreover
the weights-oriented are assigned in terms of mean square errors (MSE). Then
the WAGIF and the original GIF are compared and the experimental results demonstrate that our WAGIF achieves sharper edges and cut halo artifacts intensively. Next
an overall WAGIF-based detail-enhanced exposure fusion algorithm is illustrated. Our exposed multi-images fusion approach first extracts detailed layers via WAGIF simultaneously. These detailed layers are then synthesized to obtain a fine detail layer. Finally
the overall detailed layer is integrated to a medium fused image. Similar to the image decomposition technique in single image detail enhancement and tone mapping
each image input image sequence
which contains several of exposed images
is decomposed to a benched layer and a WAGIF-based detailed layer. Then
all detail layers of input images are synthesized to a single detail map via a specified transition function which guarantees that a pixel has a larger weight in a qualified exposed image than an under/over-exposed images. Furthermore
a detailed extraction conducted reflects weak edges or invisible under/over-exposed regions. Hence
the WAGIF-based details extraction tends to some information loss in under/over-exposed regions while each input image is decomposed each. A targeted task is employed to generate a single aggregation weight map in terms of the relationships between all input images
and then the generated weight map is applied for all images decomposition.
Result
2
Our overall detailed-enhanced exposure fusion algorithm is validated on 17 sections of classical multi-exposure image sequences and the experimental results are compared with more fusion approaches
like weighted guided image filtering (WGIF) and gradient dynamic guided image filtering (GDGIF). The optioned quantitative evaluation metrics contain information entropy (IE)
mutual information entropy (MIE)
and averaged gradient (AG). "Madison" and "Memorial" sequences are adopted in our algorithm. The "Madison" sequences based algorithm has an average increase of 0.19% in MIE
0.58% in MIE
and 13.29% in AG
respectively. Our "Memorial" sequences based algorithm has an average improvement of 0.13% in IE
1.06% in MIE
and 16.34% in AG. Furthermore
our algorithm has better edge-preserving performance and its fused images priority qualitatively.
Conclusion
2
Our demonstrated illustrations can obtain qualified preserve edges and details. Quantified evaluation metrics like IE
MIE and AG are further conducted for multi-algorithms comparison each prioritized evaluation.
Chen B and Wu S Q. 2020. Weighted aggregation for guided image filtering. Signal, Image and Video Processing, 14(3): 491-498[DOI: 10.1007/s11760-019-01579-1]
Chen K, Feng H J, Xu Z H, Li Q and Chen Y T. 2015. Fast detail-preserving exposure fusion. Journal of Zhejiang University (Engineering Edition), 49(6): 1048-1054
陈阔, 冯华君, 徐之海, 李奇, 陈跃庭. 2015. 细节保持的快速曝光融合. 浙江大学学报(工学版) 49(6): 1048-1054 [DOI: 10.3785/j.issn.1008-973X.2015.06.007]
Choi H H, Kang H S and Yun B J. 2019. Tone mapping of high dynamic range images combining co-occurrence histogram and visual salience detection. Applied Sciences, 9(21): #4658[DOI: 10.3390/app9214658]
Cui Y L, Chen A H, Yang B Q, Zhang S Q and Wang Y. 2019. Human visual perception-based multi-exposure fusion image quality assessment. Symmetry, 11(12): #1494[DOI: 10.3390/sym11121494]
Debevec P E and Malik J. 1997. Recovering high dynamic range radiance maps from photographs//Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques. [s. l.]: ACM: 369-378[ DOI: 10.1145/258734.258884 http://dx.doi.org/10.1145/258734.258884 ]
Durand F and Dorsey J. 2002. Fast bilateral filtering for the display of high-dynamic-rangeimages. ACM Transactions on Graphics, 21(3): 257-266[DOI: 10.1145/566654.566574]
Goshtasby A A. 2005. Fusion of multi-exposure images. Image and Vision Computing, 23(6): 611-618[DOI: 10.1016/j.imavis.2005.02.004]
Guo J Q, Bai X F and Yang C M. 2016. Multi-exposure image fusion based on improved guided filter algorithm. Microelectronics and Computer, 33(11): 14-19
郭剑桥, 白雪飞, 杨灿美. 2016. 基于改进的引导滤波算法的多曝光图像融合. 微电子学与计算机, 33(11): 14-19[DOI: 10.19304/j.cnki.issn1000-7180.2016.11.004]
He K M, Sun J and Tang, X. 2013. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6): 1397-1409[DOI: 10.1109/TPAMI.2012.213]
Hossny M, Nahavandi S and Creighton D. 2008. Comments on "Information measure for performance of image fusion". Electronics Letters, 44(18): 1066-1067[DOI: 10.1049/el:20081754]
Kou F, Chen W H, Wen C Y and Li Z G. 2015. Gradient domain guided image Filtering. IEEE Transactions on Image Processing, 24(11): 4528-4539[DOI: 10.1109/TIP.2015.2468183]
Kou F, Li Z G, Wen C Y and Chen W H. 2017. Multi-scale exposure fusion via gradient domain guided image filtering//Proceedings of 2017 IEEE International Conference on Multimedia and Expo (ICME). Hong Kong, China: IEEE: 1105-1110[ DOI: 10.1109/ICME.2017.8019529 http://dx.doi.org/10.1109/ICME.2017.8019529 ]
Kou F, Wei Z, Chen W H, Wu X M, Wen C Y and Li Z G. 2018. Intelligent detail enhancement for exposure fusion. IEEE Transactions on Multimedia, 20(2): 484-495[DOI: 10.1109/TMM.2017.2743988]
Li Y Y, Wang Q, Zhu Z Q and Qi G Q. 2021. Multi-exposure image fusion based on image decomposition and color prior. Journal of Image and Graphics, 26(12): 2800-2812
李嫄源, 王琴, 朱智勤, 齐观秋. 2021. 图像分解与色彩先验下的多曝光图像融合. 中国图象图形学报, 26(12): 2800-2812 [DOI:10. 11834/jig. 200295]
Li Z G and Zheng J H. 2018. Single image de-hazing using globally guided image filtering. IEEE Transactions on Image Processing, 27(1): 442-450[DOI: 10.1109/TIP.2017.2750418]
Li Z G, Zheng J H and Rahardja S. 2012. Detail-enhanced exposure fusion. IEEE Transactions on Image Processing, 21(11): 4672-4676[DOI: 10.1109/TIP.2012.2207396]
Li Z G, Zheng J H, Zhu Z J, Yao W and Wu S Q. 2015. Weighted guided image filtering. IEEE Transactions on Image Processing, 24(1): 120-129[DOI: 10.1109/TIP.2014.2371234]
Matsuoka R, Shirai K and Okuda M. 2019. Reference-based local color distribution transformation method and its application to image integration. Signal Processing: Image Communication, 76: 231-242[DOI: 10.1016/j.image.2019.04.020]
Mertens T, Kautz J and Van Reeth F. 2009. Exposure fusion: a simple and practical alternative to high dynamic range photography. Computer Graphics Forum, 28(1): 161-171[DOI: 10.1111/j.1467-8659.2008.01171.x]
Orozco R R, Martin I, Loscos C and Vasquez P P. 2012. Full high-dynamic range images for dynamic scenes//Proceedings Volume 8436, Optics, Photonics, and Digital Technologies for Multimedia Applications Ⅱ. Brussels, Belgium: SPIE: 65-80[ DOI: 10.1117/12.922825 http://dx.doi.org/10.1117/12.922825 ]
Qu Z, Huang X and Liu L. 2021. An improved algorithm of multi-exposure image fusion by detail enhancement. Multimedia Systems, 27(1): 33-44[DOI: 10.1007/s00530-020-00691-4]
Reinhard E, Stark M, Shirley P and Ferwerda J. 2002. Photographic tone reproduction for digital images. ACM Transactions on Graphics, 21(3): 267-276[DOI: 10.1145/566654.566575]
Sabir A, Khurshid K and Salman A. 2020. Segmentation-based image defogging using modified dark channel prior. EURASIP Journal on Image and Video Processing, 2020(1): #6[DOI: 10.1186/s13640-020-0493-9]
Tomasi C and Manduchi R. 1998. Bilateral filtering for gray and color images//Proceedings of the 6th International Conference on Computer Vision. Bombay, India: IEEE: 839-846[ DOI: 10.1109/ICCV.1998.710815 http://dx.doi.org/10.1109/ICCV.1998.710815 ]
Vavilin A and Jo K H. 2008. Recursive HDR image generation from differently exposed images based on local image properties//Proceedings of 2008 International Conference on Control, Automation and Systems. Seoul, Korea (South): IEEE: 2791-2796[ DOI: 10.1109/ICCAS.2008.4694234 http://dx.doi.org/10.1109/ICCAS.2008.4694234 ]
Ziegel E R. 2003. The elements of statistical learning. Technometrics, 45(3): 267-268[DOI: 10.1198/tech.2003.s770]
相关文章
相关作者
相关机构
京公网安备11010802024621
