Relevant bibliographies by topics / Multi-Exposure Fusion

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multi-Exposure Fusion'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Multi-Exposure Fusion"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Multi-Exposure Fusion"

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Saravi, Sara. "Use of Coherent Point Drift in computer vision applications." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/12548.

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This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The ide
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Shen, Xuan-Wei, and 沈軒緯. "ROI-Based Fusion of Multi-Exposure Images." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/u7wnc3.

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碩士<br>國立中正大學<br>電機工程研究所<br>102<br>In this thesis we propose a technique to blend multiple exposure images into a high-quality result, without generating a physically-based high dynamic range (HDR) image. This avoids physically influence like camera response curve or Bright change like flash. Our method is selecting the best image in the multiple exposure images for leading, and the other images for supportings. The leading mostly use directly in the result image expect where the ill-exposured region in leading image. In this region we fused the supportings to “support” the leading to have the
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Guo, Bo-Yi, and 郭柏易. "Multi-exposure image fusion using tone reproduction." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/81661073819289386294.

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碩士<br>清雲科技大學<br>電子工程所<br>99<br>The high dynamic range (HDR) imaging is a technique that allows saving intact luminance information of an image in real scene. The main disadvantage of the HDR imaging is that it requires huge memory storage and may cause difficulties in transmission. Thus, most digital cameras in current market use low dynamic range (LDR) imaging technique for image storage. However, the LDR image lacks the ability to perform intact luminance information of an image in real scene. Many researchers have developed techniques on merging several LDR images to produce a new LDR image
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Chien-Chih, Hsu. "Multi-Exposure Image Fusion for Digital Still Cameras." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0021-2004200718214103.

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Hsu, Chien-Chih, and 徐健智. "Multi-Exposure Image Fusion for Digital Still Cameras." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/03114246831843680298.

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碩士<br>國立臺灣師範大學<br>應用電子科技研究所<br>94<br>Fusing multiple frames with different exposure time can accommodate the scenes with high dynamic range. In this thesis, we propose an approach that is to fuse two consecutive video frames with different exposure time. Finding moving objects and human faces in such a higher dynamic range fused image is much easier than the typical exposed frame. The proposed approach has been implemented on a commercial digital camera with robust hardware and software platform and the experimental result shows that the fusion speed is around 4 frames/seconds. Fusing several
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LIU, TING-CHI, and 劉丁綺. "Automatic Multi-Exposure Image Fusion Based on Visual Saliency Map." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/t79r2z.

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碩士<br>國立臺北科技大學<br>自動化科技研究所<br>107<br>Due to the limitation of camera sensors, the high dynamic range imaging(HDRI) techniques are popular in recent years. Although HDRI is getting mathematically sophisticated, such as global filter or local filter of eliminating noise, pixel variation and optimization of preserving details, scientists are still looking for a good model of weight map generation for multiple-exposure image fusion which produces HDR images. In the research of human vision system, we also try to understand the fineness of image and what defines good image feature to human vision.
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Ram, Prabhakar Kathirvel. "Advances in High Dynamic Range Imaging Using Deep Learning." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5515.

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Natural scenes have a wide range of brightness, from dark starry nights to bright sunlit beaches. Our human eyes can perceive such a vast range of illumination through various adaptation techniques, thus allowing us to enjoy them. Contrarily, digital cameras can capture a limited brightness range due to their sensor limitations. Often, the dynamic range of the scene far exceeds the hardware limit of standard digital camera sensors. In such scenarios, the resulting photos will consist of saturated regions, either too dark or too bright to visually comprehend. An easy to deploy and widely used a
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Books on the topic "Multi-Exposure Fusion"

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Low Choy, Samantha, Justine Murray, Allan James, and Kerrie Mengersen. Combining monitoring data and computer model output in assessing environmental exposure. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.18.

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This article discusses an approach that combines monitoring data and computer model outputs for environmental exposure assessment. It describes the application of Bayesian data fusion methods using spatial Gaussian process models in studies of weekly wet deposition data for 2001 from 120 sites monitored by the US National Atmospheric Deposition Program (NADP) in the eastern United States. The article first provides an overview of environmental computer models, with a focus on the CMAQ (Community Multi-Scale Air Quality) Eta forecast model, before considering some algorithmic and pseudo-statist
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Book chapters on the topic "Multi-Exposure Fusion"

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May, Michael, Martin Turner, and Tim Morris. "FAW for Multi-exposure Fusion Features." In Advances in Image and Video Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25367-6_26.

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Yu, Hanyi, and Yue Zhou. "Fusion of Multi-view Multi-exposure Images with Delaunay Triangulation." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_76.

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Bhateja, Vikrant, Ashutosh Singhal, and Anil Singh. "Multi-exposure Image Fusion Method Using Anisotropic Diffusion." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1165-9_80.

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Patel, Diptiben, Bhoomika Sonane, and Shanmuganathan Raman. "Multi-exposure Image Fusion Using Propagated Image Filtering." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2104-6_39.

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Xue, Xiao, and Yue Zhou. "Multi-view Multi-exposure Image Fusion Based on Random Walks Model." In Computer Vision – ACCV 2016 Workshops. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54526-4_36.

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Jishnu, C. R., and S. Vishnukumar. "An Effective Multi-exposure Fusion Approach Using Exposure Correction and Recursive Filter." In Inventive Systems and Control. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1624-5_46.

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Biswas, Anmol, K. S. Green Rosh, and Sachin Deepak Lomte. "Spatially Variant Laplacian Pyramids for Multi-frame Exposure Fusion." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4015-8_7.

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Bai, Yuanchao, Huizhu Jia, Hengjin Liu, et al. "A Multi-exposure Fusion Method Based on Locality Properties." In Advances in Multimedia Information Processing – PCM 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13168-9_37.

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Dhivya Lakshmi, R., K. V. Rekha, E. Ilin Shantha Mary, et al. "Multi-exposure Image Reconstruction by Energy-Based Fusion Technique." In Advances in Automation, Signal Processing, Instrumentation, and Control. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_130.

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Tian, Weidong, Lifeng Zhang, and Zhongqiu Zhao. "Semantic-Aware Multi-Exposure Fusion Through Vision-Language Model." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9952-0_19.

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Conference papers on the topic "Multi-Exposure Fusion"

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Yayci, Zeynep Ovgu, and Mehmet Turkan. "Sparse Features for Multi-Exposure Fusion." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715133.

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Yang, Xuziqian, Tao Tu, Yongbin Liu, Huaian Chen, and Yi Jin. "FPGA-Based Lightweight Dual-Stage Multi-Exposure Image Fusion." In 2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2024. https://doi.org/10.1109/icsmd64214.2024.10920588.

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Arya, Prince, Saurabh Kumar, Ashish Agarwal, Nutan Yenneti, and Narasimha Pai. "Redefining Well Exposedness for Locally Adaptive Multi-Exposure Fusion." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889561.

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Mu, Pan, Binjia Zhou, Qirui Wang, Zhiying Du, and Xiaoyan Wang. "BFMEF: Brightness-Free Multi-exposure Image Fusion via Adaptive Correction." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10688315.

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Zhang, Feifei, Jianhui Xu, Ruqi Zhou, Yilin Wu, Xiongyong Zhu, and Guoming Chen. "Structure tensor based multi-exposure images fusion in gradient domain." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00296.

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Jin, Haiyan, Long Li, Haonan Su, YuanLin Zhang, ZhaoLin Xiao, and Bin Wang. "A Multi-Exposure Generation and Fusion Method for Low-Light Image Enhancement." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650454.

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Yao, Keke, Guanglong Liao, Gangyi Jiang, Yeyao Chen, and Mei Yu. "Multi-Exposure Fusion Light Field Image Quality Assessment Based on Object Detection." In 2024 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2024. https://doi.org/10.1109/icspcc62635.2024.10770492.

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Zhang, Mei, Ye Jin, and Jinhui Zhu. "DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framework using Adaptive Gabor convolution." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661847.

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Feng, Yunfei, and Mei Zhang. "DA-MEF: A Multi-exposure Fusion Network via Detail Enhancement and Global Feature Attention." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865111.

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Ma, Tengyu, and Zhitian Chen. "Multi-exposure Laser Spot Image Fusion Dataset for Simulated and Real-World Scenes and A Laser Spot Shape-Aware Fusion Algorithm." In 2024 6th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT). IEEE, 2024. http://dx.doi.org/10.1109/ecnct63103.2024.10704465.

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