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Relevant bibliographies by topics / Image fusion methods

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Academic literature on the topic 'Image fusion methods'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Image fusion methods"

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Patil, Varsha, Deepali Sale, and M. A. Joshi. "Image Fusion Methods and Quality Assessment Parameters." Asian Journal of Engineering and Applied Technology 2, no. 1 (2013): 40–45. http://dx.doi.org/10.51983/ajeat-2013.2.1.643.

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Image processing techniques primarily focus upon enhancing the quality of an image or a set of images and to derive the maximum information from them. Image Fusion is such a technique of producing a superior quality image from a set of available images. It is the process of combining relevant information from two or more images into a single image wherein the resulting image will be more informative and complete than any of the input images. A lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection, Image processing, parallel and distribute

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Sun, Changqi, Cong Zhang, and Naixue Xiong. "Infrared and Visible Image Fusion Techniques Based on Deep Learning: A Review." Electronics 9, no. 12 (2020): 2162. http://dx.doi.org/10.3390/electronics9122162.

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Infrared and visible image fusion technologies make full use of different image features obtained by different sensors, retain complementary information of the source images during the fusion process, and use redundant information to improve the credibility of the fusion image. In recent years, many researchers have used deep learning methods (DL) to explore the field of image fusion and found that applying DL has improved the time-consuming efficiency of the model and the fusion effect. However, DL includes many branches, and there is currently no detailed investigation of deep learning metho

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Zhou, Youyong, Lingjie Yu, Chao Zhi, et al. "A Survey of Multi-Focus Image Fusion Methods." Applied Sciences 12, no. 12 (2022): 6281. http://dx.doi.org/10.3390/app12126281.

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As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are l

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Elaiyaraja, K., and M. Senthil Kumar. "Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review." International Journal of Engineering & Technology 7, no. 3.12 (2018): 175. http://dx.doi.org/10.14419/ijet.v7i3.12.15913.

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Image Processing is an art to get an enriched image or it can be used to retrieve information. This image processing methods are used in medical field also. Numerous modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) etc. are used to analyze and diagnose diseases.Pixel-level image fusion is a combination of several images collected from various inputs and gives more information than any other input messages. Pixel-level image fusion shows a vital role in medical imaging. In this paper, pixel-level image fusionsmethods are survived

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Yahanda, Alexander T., Timothy J. Goble, Peter T. Sylvester, et al. "Impact of 3-Dimensional Versus 2-Dimensional Image Distortion Correction on Stereotactic Neurosurgical Navigation Image Fusion Reliability for Images Acquired With Intraoperative Magnetic Resonance Imaging." Operative Neurosurgery 19, no. 5 (2020): 599–607. http://dx.doi.org/10.1093/ons/opaa152.

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Abstract BACKGROUND Fusion of preoperative and intraoperative magnetic resonance imaging (iMRI) studies during stereotactic navigation may be very useful for procedures such as tumor resections but can be subject to error because of image distortion. OBJECTIVE To assess the impact of 3-dimensional (3D) vs 2-dimensional (2D) image distortion correction on the accuracy of auto-merge image fusion for stereotactic neurosurgical images acquired with iMRI using a head phantom in different surgical positions. METHODS T1-weighted intraoperative images of the head phantom were obtained using 1.5T iMRI.

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Raid, Rafi Omar Al-Nima, Yaseen Al-Ridha Moatasem, and Hamid Abdulraheem Farqad. "Regenerating face images from multi-spectral palm images using multiple fusion methods." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 6 (2019): 3110–19. https://doi.org/10.12928/TELKOMNIKA.v17i6.12857.

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This paper established a relationship between multi-spectral palm images and a face image based on multiple fusion methods. The first fusion method to be considered is a feature extraction between different multi-spectral palm images, where multi-spectral CASIA database was used. The second fusion method to be considered is a score fusion between two parts of an output face image. Our method suggests that both right and left hands are used, and that each hand aims to produce a significant part of a face image by using a Multi-Layer Perceptron (MLP) network. This will lead to the second fusion

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Luo, Yongyu, and Zhongqiang Luo. "Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects." Applied Sciences 13, no. 19 (2023): 10891. http://dx.doi.org/10.3390/app131910891.

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Infrared and visible light image fusion combines infrared and visible light images by extracting the main information from each image and fusing it together to provide a more comprehensive image with more features from the two photos. Infrared and visible image fusion has gained popularity in recent years and is increasingly being employed in sectors such as target recognition and tracking, night vision, scene segmentation, and others. In order to provide a concise overview of infrared and visible picture fusion, this paper first explores its historical context before outlining current domesti

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Masood, Saleha, Muhammad Sharif, Mussarat Yasmin, Muhammad Alyas Shahid, and Amjad Rehman. "Image Fusion Methods: A Survey." Journal of Engineering Science and Technology Review 10, no. 6 (2017): 186–95. http://dx.doi.org/10.25103/jestr.106.24.

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Ben-Shoshan, Yotam, and Yitzhak Yitzhaky. "Improvements of image fusion methods." Journal of Electronic Imaging 23, no. 2 (2014): 023021. http://dx.doi.org/10.1117/1.jei.23.2.023021.

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Dhakad, Basant, and Vivek Shrivastava. "Performance Improvement of Multi Image Fusion in Wavelet Domain for Medical Images." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 04 (2013): 103–7. https://doi.org/10.5281/zenodo.14594715.

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Today, image fusion as one kind of information integrated technology has played an important role in many fields. Most of previous image fusion methods aim at obtaining as many as information from the different images. But in this paper the fusion criterion is to minimize different error between the fused image and the input images. This paper presents the use of image fusion of medical images. Multi-sensor image fusion is the process of combining information from two or more images into a single image. The resulting image contains all the information of the input images. It is containing more

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Dissertations / Theses on the topic "Image fusion methods"

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Lundqvist, Roger. "Atlas-Based Fusion of Medical Brain Images : Methods and Applications." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2001. http://publications.uu.se/theses/91-554-5180-2/.

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Zhang, Qiong. "Infrared image enhancement based on hybrid-domain consideration and data fusion methods." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/26993.

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Au cours des dernières décennies, l’effort sur les applications de capteurs infrarouges a largement progressé dans le monde. Mais, une certaine difficulté demeure, en ce qui concerne le fait que les objets ne sont pas assez clairs ou ne peuvent pas toujours être distingués facilement dans l’image obtenue pour la scène observée. L’amélioration de l’image infrarouge a joué un rôle important dans le développement de technologies de la vision infrarouge de l’ordinateur, le traitement de l’image et les essais non destructifs, etc. Cette thèse traite de la question des techniques d’amélioration de l

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Ocampo, Blandon Cristian Felipe. "Patch-Based image fusion for computational photography." Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0020.

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Dans de nombreuses situations, la dynamique des capteurs ou la profondeur de champ des appareils photographiques conventionnels sont insuffisantes pour capturer fidèlement des scènes naturelles. Une méthode classique pour contourner ces limitations est de fusionner des images acquises avec des paramètres de prise de vue variables. Ces méthodes nécessitent que les images soient parfaitement alignées et que les scènes soient statiques, faute de quoi des artefacts (fantômes) ou des structures irrégulières apparaissent lors de la fusion. Le but de cette thèse est de développer des techniques perme

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SOLARNA, DAVID. "Machine Learning and Pattern Recognition Methods for Remote Sensing Image Registration and Fusion." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1039743.

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In the last decade, the remote sensing world has dramatically evolved. New types of sensor, each one collecting data with possibly different modalities, have been designed, developed, and deployed. Moreover, new missions have been planned and launched, aimed not only at collecting data of the Earth's surface, but also at acquiring planetary data in support of the study of the whole Solar system. Indeed, such a variety of technologies highlights the need for automatic methods able to effectively exploit all the available information. In the last years, lot of effort has been put in the design a

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Crow, Mason W. "Multiple sensor credit apportionment." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FCrow.pdf.

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Retief, Francois Jacques. "Methods for multi-spectral image fusion: identifying stable and repeatable information across the visible and infrared spectra." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20636.

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Fusion of images captured from different viewpoints is a well-known challenge in computer vision with many established approaches and applications; however, if the observations are captured by sensors also separated by wavelength, this challenge is compounded significantly. This dissertation presents an investigation into the fusion of visible and thermal image information from two front-facing sensors mounted side-by-side. The primary focus of this work is the development of methods that enable us to map and overlay multi-spectral information; the goal is to establish a combined image in whic

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Ylimäki, M. (Markus). "Methods for image-based 3-D modeling using color and depth cameras." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526217352.

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Abstract This work addresses the problems related to three-dimensional modeling of scenes and objects and model evaluation. The work is divided into four main parts. At first, the work concentrates on purely image-based reconstruction while the second part presents a modeling pipeline based on an active depth sensor. Then, the work introduces methods for producing surface meshes from point clouds, and finally, a novel approach for model evaluation is presented. In the first part, this work proposes a multi-view stereo (MVS) reconstruction method that takes a set of images as an input and outpu

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Vandoni, Jennifer. "Ensemble Methods for Pedestrian Detection in Dense Crowds." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS116/document.

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Cette thèse s’intéresse à la détection des piétons dans des foules très denses depuis un système mono-camera, avec comme but d’obtenir des détections localisées de toutes les personnes. Ces détections peuvent être utilisées soit pour obtenir une estimation robuste de la densité, soit pour initialiser un algorithme de suivi. Les méthodologies classiques utilisées pour la détection de piétons s’adaptent mal au cas où seulement les têtes sont visibles, de part l’absence d’arrière-plan, l’homogénéité visuelle de la foule, la petite taille des objets et la présence d’occultations très fortes. En pr

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Wei, Qi. "Bayesian fusion of multi-band images : A powerful tool for super-resolution." Phd thesis, Toulouse, INPT, 2015. http://oatao.univ-toulouse.fr/14398/1/wei.pdf.

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Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spe

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MAGGIOLO, LUCA. "Deep Learning and Advanced Statistical Methods for Domain Adaptation and Classiﬁcation of Remote Sensing Images". Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1070050.

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In the recent years, remote sensing has faced a huge evolution. The constantly growing availability of remote sensing data has opened up new opportunities and laid the foundations for many new challenges. The continuous space missions and new constellations of satellites allow in fact more and more frequent acquisitions, at increasingly higher spatial resolutions, and at an almost total coverage of the globe. The availability of such an huge amount data has highlighted the need for automatic techniques capable of processing the data and exploiting all the available information. Meanwhile, the

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Books on the topic "Image fusion methods"

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Lundqvist, Roger. Atlas-Based Fusion of Medical Brain Images: Methods and Applications (Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, 673). Uppsala Universitet, 2001.

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Book chapters on the topic "Image fusion methods"

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Mitchell, H. B. "Re-sampling Methods." In Image Fusion. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11216-4_11.

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Bloch, Isabelle, and Anca Ralescu. "Fusion." In Fuzzy Sets Methods in Image Processing and Understanding. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19425-2_5.

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Kavzoglu, Taskin, Brandt Tso, and Paul M. Mather. "Multisource Image Fusion and Classification." In Classification Methods for Remotely Sensed Data, 3rd ed. CRC Press, 2024. http://dx.doi.org/10.1201/9781003439172-4.

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Krishnan, Palani Thanaraj, and Vijayarajan Rajangam. "Image Fusion Using Deep Learning Methods." In Advanced Image Fusion Techniques for Medical Imaging. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-7602-6_3.

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Bloch, I. "Fusion of Information under Imprecision and Uncertainty, Numerical Methods, and Image Information Fusion." In Multisensor Fusion. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_11.

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Tobon, Gabriel, Jacob Hesterman, Shil Patel, and Christian Lackas. "High-Level Story: Data Analysis in Multimodal Preclinical Imaging—Methods and Tools." In Image Fusion in Preclinical Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02973-9_1.

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Ojha, Manoj. "Image Fusion Using Wavelet Transforms." In International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92905-3_22.

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Kaur, Ravpreet, and Sarbjeet Singh. "Multi-focus Image Fusion Methods: A Review." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47221-3_10.

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Bloch, Isabelle. "Fusion of Image Information under Imprecision and Uncertainty: Numerical Methods." In Data Fusion and Perception. Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-2580-9_8.

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Franek, Lucas, Daniel Duarte Abdala, Sandro Vega-Pons, and Xiaoyi Jiang. "Image Segmentation Fusion Using General Ensemble Clustering Methods." In Computer Vision – ACCV 2010. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19282-1_30.

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Conference papers on the topic "Image fusion methods"

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gong, sui rong, and lin shi. "Research on segmentation, registration, and fusion methods for multispectral tongue images." In Seventeenth International Conference on Digital Image Processing (ICDIP 2025), edited by Xudong Jiang, Jindong Tian, Ting-Chung Poon, and Zhaohui Wang. SPIE, 2025. https://doi.org/10.1117/12.3073418.

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Laidouni, Mohammed, Boban Bondžulić, Dimitrije Bujaković, Touati Adli, and Milenko Andrić. "Multisensor image fusion: Dataset, methods and performance evaluation." In 11th International Scientific Conference on Defensive Technologies - OTEX 2024. Military Technical Institute, Belgrade, 2024. http://dx.doi.org/10.5937/oteh24058z.

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Multisensor image fusion is a crucial research area aiming to enhance image clarity and comprehensibility by integrating information from multiple sensors. This paper presents a residual dense transformer (RDT) architecture for multisensor image fusion to address the challenges posed by the unique strengths and limitations of visual infrared (VIS), near-infrared (NIR), and long-wavelength infrared (LWIR) sensors. A comparative analysis is conducted with several state-of-the-art fusion methods using various objective evaluation indicators to asses the image fusion quality. We used a 313 triplet

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Ben-Shoshan, Yotam, and Yitzhak Yitzhaky. "Enhancement of image fusion methods." In SPIE Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2013. http://dx.doi.org/10.1117/12.2023224.

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Li, Qin, Caihong Dai, Xin Yu, et al. "Image fusion methods suitable for biomedical image." In Photonics China '98, edited by Junheng Li and James A. Harrington. SPIE, 1998. http://dx.doi.org/10.1117/12.317860.

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Sun, Yiming, Bing Cao, Pengfei Zhu, and Qinghua Hu. "Dynamic Brightness Adaptation for Robust Multi-modal Image Fusion." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/146.

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Infrared and visible image fusion aim to integrate modality strengths for visually enhanced, informative images. Visible imaging in real-world scenarios is susceptible to dynamic environmental brightness fluctuations, leading to texture degradation. Existing fusion methods lack robustness against such brightness perturbations, significantly compromising the visual fidelity of the fused imagery. To address this challenge, we propose the Brightness Adaptive multimodal dynamic fusion framework (BA-Fusion), which achieves robust image fusion despite dynamic brightness fluctuations. Specifically, w

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Sankowski, Dominik, Rasif Zain, and Volodymyr Mosorov. "Image fusion for dual tomography." In 2nd International Conference on Perspective Technologies and Methods in MEMS Design. IEEE, 2006. http://dx.doi.org/10.1109/memstech.2006.288668.

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Wei, Chunyu, Bingyin Zhou, and Wei Guo. "Novel fusion rules for transform-domain image fusion methods." In 2015 3rd International Conference on Mechatronics and Industrial Informatics. Atlantis Press, 2015. http://dx.doi.org/10.2991/icmii-15.2015.147.

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Sychev, A. S., and I. S. Kholopov. "Research of Multi-scale Decomposition-based Noisy Multispectral Image Fusion Methods Efficiency." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-462-469.

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The article considers algorithms of multiscale decomposition under the influence of additive noise in one of the channels of a multispectral vision system. The difference between pyramid-based and wavelet-based decomposition-reconstruction methods is shown. Structural schemes for the realization of different image fusion strategies are presented, and their advantages and disadvantages are described. To estimate the fused image quality the authors applied the complex integral-multiplicative index of digital grayscale image quality that operates with such partial indices as signal-to-noise ratio

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Wang, Di, Jinyuan Liu, Xin Fan, and Risheng Liu. "Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/487.

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Recent learning-based image fusion methods have marked numerous progress in pre-registered multi-modality data, but suffered serious ghosts dealing with misaligned multi-modality data, due to the spatial deformation and the difficulty narrowing cross-modality discrepancy. To overcome the obstacles, in this paper, we present a robust cross-modality generation-registration paradigm for unsupervised misaligned infrared and visible image fusion (IVIF). Specifically, we propose a Cross-modality Perceptual Style Transfer Network (CPSTN) to generate a pseudo infrared image taking a visible image as i

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Malviya, A., and S. G. Bhirud. "Wavelet based multi-focus image fusion." In 2009 International Conference on Methods and Models in Computer Science (ICM2CS). IEEE, 2009. http://dx.doi.org/10.1109/icm2cs.2009.5397990.

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Reports on the topic "Image fusion methods"

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Willsky, Alan S. Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425745.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, s

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Beauchemin, M., K. B. Fung, and X. Geng. A Method Based on Local Variance for Quality Assessment of Multiresolution Image Fusion. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219921.

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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no

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