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Journal articles on the topic 'Multi-Exposure Fusion'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Goshtasby, A. Ardeshir. "Fusion of multi-exposure images." Image and Vision Computing 23, no. 6 (2005): 611–18. http://dx.doi.org/10.1016/j.imavis.2005.02.004.

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Deng, Chenwei, Zhen Li, Shuigen Wang, Xun Liu, and Jiahui Dai. "Saturation-based quality assessment for colorful multi-exposure image fusion." International Journal of Advanced Robotic Systems 14, no. 2 (2017): 172988141769462. http://dx.doi.org/10.1177/1729881417694627.

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Multi-exposure image fusion is becoming increasingly influential in enhancing the quality of experience of consumer electronics. However, until now few works have been conducted on the performance evaluation of multi-exposure image fusion, especially colorful multi-exposure image fusion. Conventional quality assessment methods for multi-exposure image fusion mainly focus on grayscale information, while ignoring the color components, which also convey vital visual information. We propose an objective method for the quality assessment of colored multi-exposure image fusion based on image saturat

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3

Xiang, Hu Yan, and Xi Rong Ma. "An Improved Multi-Exposure Image Fusion Algorithm." Advanced Materials Research 403-408 (November 2011): 2200–2205. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2200.

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An improved Multi-Exposure image fusion scheme is proposed to fuse visual images for wide range illumination applications. While previous image fusion approaches perform the fusion only concern with local details such as regional contrast and gradient, the proposed algorithm takes global illumination contrast into consideration at the same time; this can extend the dynamic range evidently. Wavelet is used as Multi-Scale analysis tool in intensity fusion. For color fusion, HSI color model and weight map based method is used. The experimental results showed that the proposed fusion scheme has si

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LI Wei-zhong, 李卫中, 易本顺 YI Ben-shun, 邱. 康. QIU Kang, and 彭. 红. PENG Hong. "Detail preserving multi-exposure image fusion." Optics and Precision Engineering 24, no. 9 (2016): 2283–92. http://dx.doi.org/10.3788/ope.20162409.2283.

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5

Shaikh, Uzmanaz A., Vivek J. Vishwakarma, and Shubham S. Mahale. "Dynamic Scene Multi-Exposure Image Fusion." IETE Journal of Education 59, no. 2 (2018): 53–61. http://dx.doi.org/10.1080/09747338.2018.1510744.

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6

Li, Zhengguo, Zhe Wei, Changyun Wen, and Jinghong Zheng. "Detail-Enhanced Multi-Scale Exposure Fusion." IEEE Transactions on Image Processing 26, no. 3 (2017): 1243–52. http://dx.doi.org/10.1109/tip.2017.2651366.

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Inoue, Kohei, Hengjun Yu, Kenji Hara, and Kiichi Urahama. "Saturation-Enhancing Multi-Exposure Image Fusion." Journal of the Institute of Image Information and Television Engineers 70, no. 8 (2016): J185—J187. http://dx.doi.org/10.3169/itej.70.j185.

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8

Liu, Renshuai, Chengyang Li, Haitao Cao, Yinglin Zheng, Ming Zeng, and Xuan Cheng. "EMEF: Ensemble Multi-Exposure Image Fusion." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1710–18. http://dx.doi.org/10.1609/aaai.v37i2.25259.

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Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy. In this paper, we study the MEF problem from a new perspective. We don’t utilize any synthesized ground truth, design any loss function, or develop any fusion strategy. Our proposed method EMEF takes advantage of the wisdom of multiple imperfect MEF contributors including both conventional and deep learning-based methods. Specifically, EMEF consists of two main stages: pre-tra

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CM, Sushmitha, and Meharunnisa SP. "An Image Quality Assessment of Multi-Exposure Image Fusion by Improving SSIM." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 2780–84. http://dx.doi.org/10.31142/ijtsrd15634.

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10

Hayat, Naila, and Muhammad Imran. "Multi-exposure image fusion technique using multi-resolution blending." IET Image Processing 13, no. 13 (2019): 2554–61. http://dx.doi.org/10.1049/iet-ipr.2019.0438.

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11

Paul, Sujoy, Ioana S. Sevcenco, and Panajotis Agathoklis. "Multi-Exposure and Multi-Focus Image Fusion in Gradient Domain." Journal of Circuits, Systems and Computers 25, no. 10 (2016): 1650123. http://dx.doi.org/10.1142/s0218126616501231.

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A multi-exposure and multi-focus image fusion algorithm is proposed. The algorithm is developed for color images and is based on blending the gradients of the luminance components of the input images using the maximum gradient magnitude at each pixel location and then obtaining the fused luminance using a Haar wavelet-based image reconstruction technique. This image reconstruction algorithm is of [Formula: see text] complexity and includes a Poisson solver at each resolution to eliminate artifacts that may appear due to the nonconservative nature of the resulting gradient. The fused chrominanc

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12

Song, Changho, Soowoong Jeong, and Sangkeun Lee. "Colorful Multi-Exposure Fusion with Guided Filtering based Fusion Method." TECHART: Journal of Arts and Imaging Science 3, no. 4 (2016): 27. http://dx.doi.org/10.15323/techart.2016.11.3.4.27.

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13

LIU Xin-long, 刘鑫龙, and 易红伟 YI Hong-wei. "Improved Multi-exposure Image Pyramid Fusion Method." ACTA PHOTONICA SINICA 48, no. 8 (2019): 810002. http://dx.doi.org/10.3788/gzxb20194808.0810002.

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14

Babu, K. Suresh, S. Ali Asgar, Dr V. Thtimurthulu, and MSA Srivatsava. "Multi Exposure Image Fusion based on PCA." International Journal of Engineering Research and Advanced Technology 4, no. 8 (2018): 37–46. http://dx.doi.org/10.31695/ijerat.2018.3296.

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15

Chen, Bin, Xincheng Tan, and Shiqian Wu. "Overall detail-enhanced multi-exposure images fusion." Journal of Image and Graphics 27, no. 5 (2022): 1632–44. http://dx.doi.org/10.11834/jig.210036.

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16

Yan, Qingsen, Yu Zhu, Yulin Zhou, Jinqiu Sun, Lei Zhang, and Yanning Zhang. "Enhancing image visuality by multi-exposure fusion." Pattern Recognition Letters 127 (November 2019): 66–75. http://dx.doi.org/10.1016/j.patrec.2018.10.008.

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17

Ahmad, Attiq, Muhammad Mohsin Riaz, Abdul Ghafoor, and Tahir Zaidi. "Noise Resistant Fusion for Multi-Exposure Sensors." IEEE Sensors Journal 16, no. 13 (2016): 5123–24. http://dx.doi.org/10.1109/jsen.2016.2556715.

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18

Shouhong, Chen, Zhao Shuang, Ma Jun, Liu Xinyu, and Hou Xingna. "A Multi-exposure Image Fusion Method with Detail Preservation." MATEC Web of Conferences 173 (2018): 03009. http://dx.doi.org/10.1051/matecconf/201817303009.

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In view of the problems of uneven exposure in the image acquisition and the serious loss of details in the traditional multi-exposure image fusion algorithm, a method of image fusion with details preservation is proposed. A weighted approach to multi-exposure image fusion is used, taking into account the features such as local contrast, exposure brightness, and color information to better preserve detail. For the purpose of eliminating the noise and interference, using the recursive filter to filter. Compared with other algorithms, the proposed algorithm can retain the rich detail information

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19

Hu, Yunxue, Chao Xu, Zhengping Li, et al. "Detail Enhancement Multi-Exposure Image Fusion Based on Homomorphic Filtering." Electronics 11, no. 8 (2022): 1211. http://dx.doi.org/10.3390/electronics11081211.

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Due to the large dynamic range of real scenes, it is difficult for images taken by ordinary devices to represent high-quality real scenes. To obtain high-quality images, the exposure fusion of multiple exposure images of the same scene is required. The fusion of multiple images results in the loss of edge detail in areas with large exposure differences. Aiming at this problem, this paper proposes a new method for the fusion of multi-exposure images with detail enhancement based on homomorphic filtering. First, a fusion weight map is constructed using exposure and local contrast. The exposure w

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20

Han, Yongcheng, Wenwen Zhang, and Weiji He. "Low-light image enhancement based on simulated multi-exposure fusion." Journal of Physics: Conference Series 2478, no. 6 (2023): 062022. http://dx.doi.org/10.1088/1742-6596/2478/6/062022.

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Abstract We propose an efficient and novel framework for low-light image enhancement, which aims to reveal information hidden in the darkness and improve overall brightness and local contrast. Inspired by exposure fusion technique, we employ simulated multi-exposure images fusion to derive bright, natural and satisfactory results, while images are taken under poor conditions such as insufficient or uneven illumination, back-lit and limited exposure time. Specifically, we first design a novel method to generate synthesized images with varying exposure time from a single image. Thus, each image

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21

Buades, Antoni, Jose Luis Lisani, and Onofre Martorell. "Efficient joint noise removal and multi exposure fusion." PLOS ONE 17, no. 3 (2022): e0265464. http://dx.doi.org/10.1371/journal.pone.0265464.

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Multi-exposure fusion (MEF) is a technique that combines different snapshots of the same scene, captured with different exposure times, into a single image. This combination process (also known as fusion) is performed in such a way that the parts with better exposure of each input image have a stronger influence. Therefore, in the result image all areas are well exposed. In this paper, we propose a new method that performs MEF and noise removal. Rather than denoising each input image individually and then fusing the obtained results, the proposed strategy jointly performs fusion and denoising

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22

Dharmika, A., and Gnanapriya M. "Multi-exposure Image Fusion using Patch-based Component Decomposition." International Journal of Emerging Research in Engineering, Science, and Management 1, no. 2 (2022): 18–25. https://doi.org/10.58482/ijeresm.v1i2.4.

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Multi exposure image fusion is always a challenge in task in image processing. The multiple images with the different image content, when mixed using a fusion formula generate different effects. One of the most prominent effects is ghosting effect. Ghost in effect occur even in capturing of images. The smallest ghosting effect may be treated as image blur. To handle ghosting effect as well as many other affects that are generated in the process of fusion are treated in the proposed technique. The proposal scheme introduces a completely new representation that may be explorer for the for many d

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23

Xu, Han, Liang Haochen, and Jiayi Ma. "Unsupervised Multi-Exposure Image Fusion Breaking Exposure Limits via Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3010–17. http://dx.doi.org/10.1609/aaai.v37i3.25404.

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This paper proposes an unsupervised multi-exposure image fusion (MEF) method via contrastive learning, termed as MEF-CL. It breaks exposure limits and performance bottleneck faced by existing methods. MEF-CL firstly designs similarity constraints to preserve contents in source images. It eliminates the need for ground truth (actually not exist and created artificially) and thus avoids negative impacts of inappropriate ground truth on performance and generalization. Moreover, we explore a latent feature space and apply contrastive learning in this space to guide fused image to approximate norma

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24

Qu, Linhao, Shaolei Liu, Manning Wang, and Zhijian Song. "TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 2126–34. http://dx.doi.org/10.1609/aaai.v36i2.20109.

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In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning; through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features

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25

Zhang, Xingchen. "Benchmarking and comparing multi-exposure image fusion algorithms." Information Fusion 74 (October 2021): 111–31. http://dx.doi.org/10.1016/j.inffus.2021.02.005.

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26

PIAO Yong-jie, 朴永杰, 徐伟 XU Wei, 王绍举 WANG Shao-ju, and 陶淑苹 TAO Shu-ping. "Fast multi-exposure image fusion for HDR video." Chinese Journal of Liquid Crystals and Displays 29, no. 6 (2014): 1032–41. http://dx.doi.org/10.3788/yjyxs20142906.1032.

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27

Han, Dong, Liang Li, Xiaojie Guo, and Jiayi Ma. "Multi-exposure image fusion via deep perceptual enhancement." Information Fusion 79 (March 2022): 248–62. http://dx.doi.org/10.1016/j.inffus.2021.10.006.

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28

Xu, Fang, Jinghong Liu, Yueming Song, Hui Sun, and Xuan Wang. "Multi-Exposure Image Fusion Techniques: A Comprehensive Review." Remote Sensing 14, no. 3 (2022): 771. http://dx.doi.org/10.3390/rs14030771.

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Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image processing and computer vision, which can integrate images with multiple exposure levels into a full exposure image of high quality. It is an economical and effective way to improve the dynamic range of the imaging system and has broad application prospects. In recent years, with the further development of image representation theories such as multi-scale analysis and deep learning, significant progress has been achieved in this field. This paper comprehensively investigates the current research status o

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29

Ma, Kede, Kai Zeng, and Zhou Wang. "Perceptual Quality Assessment for Multi-Exposure Image Fusion." IEEE Transactions on Image Processing 24, no. 11 (2015): 3345–56. http://dx.doi.org/10.1109/tip.2015.2442920.

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30

Zhang, Wenlong, Xiaolin Liu, Wuchao Wang, and Yujun Zeng. "Multi-exposure image fusion based on wavelet transform." International Journal of Advanced Robotic Systems 15, no. 2 (2018): 172988141876893. http://dx.doi.org/10.1177/1729881418768939.

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31

Choi, Seungcheol, Oh-Jin Kwon, and Jinhee Lee. "A Method for Fast Multi-Exposure Image Fusion." IEEE Access 5 (2017): 7371–80. http://dx.doi.org/10.1109/access.2017.2694038.

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32

Liu, Yu, and Zengfu Wang. "Dense SIFT for ghost-free multi-exposure fusion." Journal of Visual Communication and Image Representation 31 (August 2015): 208–24. http://dx.doi.org/10.1016/j.jvcir.2015.06.021.

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33

Wu, Shengcong, Ting Luo, Yang Song, and Haiyong Xu. "Multi-exposure image fusion based on tensor decomposition." Multimedia Tools and Applications 79, no. 33-34 (2020): 23957–75. http://dx.doi.org/10.1007/s11042-020-09131-x.

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34

Martorell, Onofre, Catalina Sbert, and Antoni Buades. "Ghosting-free DCT based multi-exposure image fusion." Signal Processing: Image Communication 78 (October 2019): 409–25. http://dx.doi.org/10.1016/j.image.2019.07.020.

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35

Tan, Xiao, Huaian Chen, Rui Zhang, et al. "Deep Multi-Exposure Image Fusion for Dynamic Scenes." IEEE Transactions on Image Processing 32 (2023): 5310–25. http://dx.doi.org/10.1109/tip.2023.3315123.

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36

Qi, Yanjie, Zehui Yang, and Lin Kang. "Multi-exposure X-ray image fusion quality evaluation based on CSF and gradient amplitude similarity." Journal of X-Ray Science and Technology 29, no. 4 (2021): 697–709. http://dx.doi.org/10.3233/xst-210871.

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Due to the limitation of dynamic range of the imaging device, the fixed-voltage X-ray images often produce overexposed or underexposed regions. Some structure information of the composite steel component is lost. This problem can be solved by fusing the multi-exposure X-ray images taken by using different voltages in order to produce images with more detailed structures or information. Due to the lack of research on multi-exposure X-ray image fusion technology, there is no evaluation method specially for multi-exposure X-ray image fusion. For the multi-exposure X-ray fusion images obtained by

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37

Zhang, Jiebin, Shangyou Zeng, Ying Wang, Jinjin Wang, and Hongyang Chen. "An Efficient Extreme-Exposure Image Fusion Method." Journal of Physics: Conference Series 2137, no. 1 (2021): 012061. http://dx.doi.org/10.1088/1742-6596/2137/1/012061.

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Abstract Since the existing commercial imaging equipment cannot meet the requirements of high dynamic range, multi-exposure image fusion is an economical and fast method to implement HDR. However, the existing multi-exposure image fusion algorithms have the problems of long fusion time and large data storage. We propose an extreme exposure image fusion method based on deep learning. In this method, two extreme exposure image sequences are sent to the network, channel and spatial attention mechanisms are introduced to automatically learn and optimize the weights, and the optimal fusion weights

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38

Zhu Xinli, 祝新力, 张雅声 Zhang Yasheng, 方宇强 Fang Yuqiang, 张喜涛 Zhang Xitao, 许洁平 Xu Jieping та 罗迪 Luo Di. "多曝光图像融合方法综述". Laser & Optoelectronics Progress 60, № 22 (2023): 2200003. http://dx.doi.org/10.3788/lop230683.

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Meng, Yan, Guanyi Li, and Wei Huang. "Adaptive Shadow Compensation Method in Hyperspectral Images via Multi-Exposure Fusion and Edge greenFusion." Applied Sciences 14, no. 9 (2024): 3890. http://dx.doi.org/10.3390/app14093890.

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Shadows in hyperspectral images lead to reduced spectral intensity and changes in spectral characteristics, significantly hindering analysis and applications. However, current shadow compensation methods face the issue of nonlinear attenuation at different wavelengths and unnatural transitions at the shadow boundary. To address these challenges, we propose a two-stage shadow compensation method based on multi-exposure fusion and edge fusion. Initially, shadow regions are identified through color space conversion and an adaptive threshold. The first stage utilizes multi-exposure, generating a s

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40

Jia, Jinquan, Jian Sun, and Zhiqin Zhu. "A multi-scale patch-wise algorithm for multi-exposure image fusion." Optik 248 (December 2021): 168120. http://dx.doi.org/10.1016/j.ijleo.2021.168120.

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41

Li, Hui, Kede Ma, Hongwei Yong, and Lei Zhang. "Fast Multi-Scale Structural Patch Decomposition for Multi-Exposure Image Fusion." IEEE Transactions on Image Processing 29 (2020): 5805–16. http://dx.doi.org/10.1109/tip.2020.2987133.

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42

Im, Chan-Gi, Dong-Min Son, Hyuk-Ju Kwon, and Sung-Hak Lee. "Multi-Task Learning Approach Using Dynamic Hyperparameter for Multi-Exposure Fusion." Mathematics 11, no. 7 (2023): 1620. http://dx.doi.org/10.3390/math11071620.

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High-dynamic-range (HDR) image synthesis is a technology developed to accurately reproduce the actual scene of an image on a display by extending the dynamic range of an image. Multi-exposure fusion (MEF) technology, which synthesizes multiple low-dynamic-range (LDR) images to create an HDR image, has been developed in various ways including pixel-based, patch-based, and deep learning-based methods. Recently, methods to improve the synthesis quality of images using deep-learning-based algorithms have mainly been studied in the field of MEF. Despite the various advantages of deep learning, deep

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43

汪, 嘉欣. "A Fast Multi-Exposure Fusion Algorithm for Ultra Depth of Field Fusion." Modeling and Simulation 13, no. 03 (2024): 3797–806. http://dx.doi.org/10.12677/mos.2024.133346.

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44

Peng, Yan-Tsung, He-Hao Liao, and Ching-Fu Chen. "Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction." Sensors 22, no. 1 (2021): 24. http://dx.doi.org/10.3390/s22010024.

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In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-expos

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45

Liu, Jie, and Yuanyuan Peng. "Research on Image Enhancement Algorithm Based on Artificial Intelligence." Journal of Physics: Conference Series 2074, no. 1 (2021): 012024. http://dx.doi.org/10.1088/1742-6596/2074/1/012024.

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Abstract With the continuous development of social science and technology, people have higher and higher requirements for image quality. This paper integrates artificial intelligence technology and proposes a low-illuminance panoramic image enhancement algorithm based on simulated multi-exposure fusion. First, the image information content is used as a metric to estimate the optimal exposure rate, and the brightness mapping function is used to enhance the V component, and the low-illuminance. The image and the overexposed image are input, the medium exposure image is synthesized by the exposur

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46

Gao, Mingyu, Junfan Wang, Yi Chen, Chenjie Du, Chao Chen, and Yu Zeng. "An Improved Multi-Exposure Image Fusion Method for Intelligent Transportation System." Electronics 10, no. 4 (2021): 383. http://dx.doi.org/10.3390/electronics10040383.

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In this paper, an improved multi-exposure image fusion method for intelligent transportation systems (ITS) is proposed. Further, a new multi-exposure image dataset for traffic signs, TrafficSign, is presented to verify the method. In the intelligent transportation system, as a type of important road information, traffic signs are fused by this method to obtain a fused image with moderate brightness and intact information. By estimating the degree of retention of different features in the source image, the fusion results have adaptive characteristics similar to that of the source image. Conside

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KINOSHITA, Yuma, Sayaka SHIOTA, and Hitoshi KIYA. "A Pseudo Multi-Exposure Fusion Method Using Single Image." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E101.A, no. 11 (2018): 1806–14. http://dx.doi.org/10.1587/transfun.e101.a.1806.

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48

Ryu, Je-Ho, Jong-Han Kim, and Jong-Ok Kim. "Deep Gradual Multi-Exposure Fusion Via Recurrent Convolutional Network." IEEE Access 9 (2021): 144756–67. http://dx.doi.org/10.1109/access.2021.3122540.

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49

Kim, Jong-Han, Je-Ho Ryu, and Jong-Ok Kim. "FDD-MEF: Feature-Decomposition-Based Deep Multi-Exposure Fusion." IEEE Access 9 (2021): 164551–61. http://dx.doi.org/10.1109/access.2021.3134316.

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50

Singh, Harbinder, Gabriel Cristobal, Gloria Bueno, et al. "Multi-exposure microscopic image fusion-based detail enhancement algorithm." Ultramicroscopy 236 (June 2022): 113499. http://dx.doi.org/10.1016/j.ultramic.2022.113499.

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