Relevant bibliographies by topics / Hyperspectral image reconstruction

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Hyperspectral image reconstruction'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Hyperspectral image reconstruction"

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Liu, L., L. Xu, and J. Peng. "3D RECONSTRUCTION FROM UAV-BASED HYPERSPECTRAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1073–77. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1073-2018.

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Reconstructing the 3D profile from a set of UAV-based images can obtain hyperspectral information, as well as the 3D coordinate of any point on the profile. Our images are captured from the Cubert UHD185 (UHD) hyperspectral camera, which is a new type of high-speed onboard imaging spectrometer. And it can get both hyperspectral image and panchromatic image simultaneously. The panchromatic image have a higher spatial resolution than hyperspectral image, but each hyperspectral image provides considerable information on the spatial spectral distribution of the object. Thus there is an opportunity to derive a high quality 3D point cloud from panchromatic image and considerable spectral information from hyperspectral image. The purpose of this paper is to introduce our processing chain that derives a database which can provide hyperspectral information and 3D position of each point. First, We adopt a free and open-source software, Visual SFM which is based on structure from motion (SFM) algorithm, to recover 3D point cloud from panchromatic image. And then get spectral information of each point from hyperspectral image by a self-developed program written in MATLAB. The production can be used to support further research and applications.
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Wang, Li, and Wei Wang. "Hyperspectral Image Reconstruction Based on Reference Point Nondominated Sorting Genetic Algorithm." Mobile Information Systems 2022 (April 5, 2022): 1–24. http://dx.doi.org/10.1155/2022/8455150.

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Spatial and spectral features of hyperspectral imagery reconstruction have gained increasing attention in the latest years. Based on the study of orthogonal matching pursuit (OMP) idea, a hyperspectral image reconstruction algorithm based on reference point nondominated sorting genetic algorithm (NSGA) is proposed. Instead of directly reconstructing the entire hyperspectral data as a traditional OMP reconstruction algorithm, the proposed algorithm explores the idea of the evolution process in the reconstruction. The Gabor redundancy dictionary is established as the sparse basis of hyperspectral images, and the reconstruction model of multiobjective optimization is constructed. In the reconstruction process, the NSGA-III algorithm is used to find the optimal atoms to represent the original signal, and Hermitian inversion lemma is also used to realize the recursive update of the residuals. The initial solution generation, the definition of reference points, the association and niche-preservation operation, and the crossover and mutation operation in NSGA-III are presented in detail. Experimental results on hyperspectral data demonstrate that the proposed algorithm could maintain the reconstruction accuracy, as well as the computational efficiency, and are superior to the state-of-the-art reconstruction algorithms. The proposed algorithm could be applied in the classification and unmixing in hyperspectral images.
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Sun, Shasha, Wenxing Bao, Kewen Qu, Wei Feng, Xiaowu Zhang, and Xuan Ma. "Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition." Remote Sensing 15, no. 20 (2023): 4983. http://dx.doi.org/10.3390/rs15204983.

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This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution. This algorithm seamlessly integrates graph regularization and tensor ring decomposition, presenting an innovative fusion model that effectively leverages the spatial structure and spectral information inherent in hyperspectral images. At the core of the algorithm lies an iterative optimization process embedded within the objective function. This iterative process incrementally refines latent feature representations. It incorporates spatial smoothness constraints and graph regularization terms to enhance the quality of super-resolution reconstruction and preserve image features. Specifically, low-resolution hyperspectral images (HSIs) and high-resolution multispectral images (MSIs) are obtained through spatial and spectral downsampling, which are then treated as nodes in a constructed graph, efficiently fusing spatial and spectral information. By utilizing tensor ring decomposition, HSIs and MSIs undergo feature decomposition, and the objective function is formulated to merge reconstructed results with the original images. Through a multi-stage iterative optimization procedure, the algorithm progressively enhances latent feature representations, leading to super-resolution hyperspectral image reconstruction. The algorithm’s significant achievements are demonstrated through experiments, producing sharper, more detailed high-resolution hyperspectral images (HRIs) with an improved reconstruction quality and retained spectral information. By combining the advantages of graph regularization and tensor ring decomposition, the proposed algorithm showcases substantial potential and feasibility within the domain of hyperspectral image super-resolution.
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Zhao, Jiangsan, Dmitry Kechasov, Boris Rewald, et al. "Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters." Remote Sensing 12, no. 19 (2020): 3258. http://dx.doi.org/10.3390/rs12193258.

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Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.
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Han, Xian-Hua, Yinqiang Zheng, and Yen-Wei Chen. "Hyperspectral Image Reconstruction Using Multi-scale Fusion Learning." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (2022): 1–21. http://dx.doi.org/10.1145/3477396.

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Hyperspectral imaging is a promising imaging modality that simultaneously captures several images for the same scene on narrow spectral bands, and it has made considerable progress in different fields, such as agriculture, astronomy, and surveillance. However, the existing hyperspectral (HS) cameras sacrifice the spatial resolution for providing the detail spectral distribution of the imaged scene, which leads to low-resolution (LR) HS images compared with the common red-green-blue (RGB) images. Generating a high-resolution HS (HR-HS) image via fusing an observed LR-HS image with the corresponding HR-RGB image has been actively studied. Existing methods for this fusing task generally investigate hand-crafted priors to model the inherent structure of the latent HR-HS image, and they employ optimization approaches for solving it. However, proper priors for different scenes can possibly be diverse, and to figure it out for a specific scene is difficult. This study investigates a deep convolutional neural network (DCNN)-based method for automatic prior learning, and it proposes a novel fusion DCNN model with multi-scale spatial and spectral learning for effectively merging an HR-RGB and LR-HS images. Specifically, we construct an U-shape network architecture for gradually reducing the feature sizes of the HR-RGB image (Encoder-side) and increasing the feature sizes of the LR-HS image (Decoder-side), and we fuse the HR spatial structure and the detail spectral attribute in multiple scales for tackling the large resolution difference in spatial domain of the observed HR-RGB and LR-HS images. Then, we employ multi-level cost functions for the proposed multi-scale learning network to alleviate the gradient vanish problem in long-propagation procedure. In addition, for further improving the reconstruction performance of the HR-HS image, we refine the predicted HR-HS image using an alternating back-projection method for minimizing the reconstruction errors of the observed LR-HS and HR-RGB images. Experiments on three benchmark HS image datasets demonstrate the superiority of the proposed method in both quantitative values and visual qualities.
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Liu, Lei, Jingwen Yan, Di Guo, Yunsong Liu, and Xiaobo Qu. "Undersampled Hyperspectral Image Reconstruction Based on Surfacelet Transform." Journal of Sensors 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/256391.

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Hyperspectral imaging is a crucial technique for military and environmental monitoring. However, limited equipment hardware resources severely affect the transmission and storage of a huge amount of data for hyperspectral images. This limitation has the potentials to be solved by compressive sensing (CS), which allows reconstructing images from undersampled measurements with low error. Sparsity and incoherence are two essential requirements for CS. In this paper, we introduce surfacelet, a directional multiresolution transform for 3D data, to sparsify the hyperspectral images. Besides, a Gram-Schmidt orthogonalization is used in CS random encoding matrix, two-dimensional and three-dimensional orthogonal CS random encoding matrixes and a patch-based CS encoding scheme are designed. The proposed surfacelet-based hyperspectral images reconstruction problem is solved by a fast iterative shrinkage-thresholding algorithm. Experiments demonstrate that reconstruction of spectral lines and spatial images is significantly improved using the proposed method than using conventional three-dimensional wavelets, and growing randomness of encoding matrix can further improve the quality of hyperspectral data. Patch-based CS encoding strategy can be used to deal with large data because data in different patches can be independently sampled.
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Cang, Sheng, and Achuan Wang. "Research on Hyperspectral Image Reconstruction Based on GISMT Compressed Sensing and Interspectral Prediction." International Journal of Optics 2020 (January 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/7160390.

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Hyperspectral remote-sensing images have the characteristics of large transmission data and high propagation requirements, so they are faced with transmission and preservation problems in the process of transmission. In view of this situation, this paper proposes a spectral image reconstruction algorithm based on GISMT compressed sensing and interspectral prediction. Firstly, according to the high spectral correlation of hyperspectral remote-sensing images, the hyperspectral images are grouped according to the band, and a standard band is determined in each group. The standard band in each group is weighted by the GISMT compressed sensing method. Then, a prediction model of the general band in each group is established to realize the remote-sensing image reconstruction in the general band. Finally, the difference between the actual measured value and the predicted value is calculated. According to the prediction algorithm, the corresponding difference vector is obtained and the predicted measured value is iteratively updated by the difference vector until the hyperspectral reconstructed image of the relevant general band is finally reconstructed. It is shown by experiments that this method can effectively improve the reconstruction effect of hyperspectral images.
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García-Sánchez, Ignacio, Óscar Fresnedo, José P. González-Coma, and Luis Castedo. "Coded Aperture Hyperspectral Image Reconstruction." Sensors 21, no. 19 (2021): 6551. http://dx.doi.org/10.3390/s21196551.

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In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.
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Jong, Lynn-Jade S., Jelmer G. C. Appelman, Henricus J. C. M. Sterenborg, Theo J. M. Ruers, and Behdad Dashtbozorg. "Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images." Sensors 24, no. 5 (2024): 1567. http://dx.doi.org/10.3390/s24051567.

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(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial–spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor’s reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.
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Zhang, Yan, Lifu Zhang, Ruoxi Song, and Qingxi Tong. "A General Deep Learning Point–Surface Fusion Framework for RGB Image Super-Resolution." Remote Sensing 16, no. 1 (2023): 139. http://dx.doi.org/10.3390/rs16010139.

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Hyperspectral images are usually acquired in a scanning-based way, which can cause inconvenience in some situations. In these cases, RGB image spectral super-resolution technology emerges as an alternative. However, current mainstream spectral super-resolution methods aim to generate continuous spectral information at a very narrow range, limited to the visible light range. Some researchers introduce hyperspectral images as auxiliary data. But it is usually required that the auxiliary hyperspectral images have the same spatial range as RGB images. To address this issue, a general point–surface data fusion method is designed to achieve the RGB image spectral super-resolution goal in this paper, named GRSS-Net. The proposed method utilizes hyperspectral point data as auxiliary data to provide spectral reference information. Thus, the spectral super-resolution can extend the spectral reconstruction range according to spectral data. The proposed method utilizes compressed sensing theory as a fundamental physical mechanism and then unfolds the traditional hyperspectral image reconstruction optimization problem into a deep network. Finally, a high-spatial-resolution hyperspectral image can be obtained. Thus, the proposed method combines the non-linear feature extraction ability of deep learning and the interpretability of traditional physical models simultaneously. A series of experiments demonstrates that the proposed method can effectively reconstruct spectral information in RGB images. Meanwhile, the proposed method provides a framework of spectral super-resolution for different applications.
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Dissertations / Theses on the topic "Hyperspectral image reconstruction"

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Larusson, Fridrik. "Shape-based image reconstruction methods for hyperspectral diffuse optical tomography." Thesis, Tufts University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3557529.

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<p> Diffuse optical tomography (DOT) is an optical imaging modality that uses near infrared light to recover functional information of tissue. In this thesis we focus on breast imaging where estimation of the optical properties of the breast can assist in detecting cancerous tumors and in judging overall breast health. </p><p> To this end we explore the application of a parametric level set method (PaLS) for image reconstruction for hyperspectral DOT. Chromophore concentrations and diffusion amplitude are recovered using a linearized Born approximation model and employing data from over 100 wavelengths. The images to be recovered are taken to be piecewise constant and a newly introduced, shape-based model is used as the foundation for reconstruction. The PaLS method significantly reduces the number of unknowns relative to more traditional level-set reconstruction methods and has been shown to be particularly well suited for ill-posed inverse problems such as the one of interest here. We extend the PaLS method to imaging problems by considering a redundant dictionary matrix for basis functions allowing for recovery of a wide array of shapes. </p><p> Additionally we explore the ability of diffuse optical tomography (DOT) to recover 3D tubular shapes representing vascular structures in breast tissue. Using the PaLS method, we incorporate the connectedness of vascular structures in breast tissue to reconstruct shape and absorption values from severely limited data sets. The approach is based on a decomposition of the unknown structure into a series of two dimensional slices. Using a simplified physical model that ignores 3D effects of the complete structure, we develop a novel inter-slice regularization strategy to obtain global regularity. We report on simulated and experimental reconstructions using realistic optical contrasts where our method provides a more accurate estimation compared to an unregularized approach and a pixel based reconstruction.</p>
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Fountanas, Leonidas. "Principal components based techniques for hyperspectral image data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FFountanas.pdf.

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Parmar, Manu Reeves Stanley J. "Sample selection and reconstruction for array-based multispectral imaging." Auburn, Ala., 2007. http://hdl.handle.net/10415/1368.

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Dalla, Vedova Gaetan. "Imagerie et analyse hyperspectrales d'observations interférométriques d'environnement circumstellaires." Thesis, Université Côte d'Azur (ComUE), 2016. http://www.theses.fr/2016AZUR4060/document.

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L'observation des planètes extrasolaires, ainsi que l'étude de l'environnementcircumstellaire demandent des instruments très performants en matière dedynamique et de résolution angulaire. L'interférométrie classique et annulanteoffrent une solution. En particulier, dans le cas de l'interférométrie annulante,le flux de l'étoile sur l'axe de l'interféromètre est fortement réduit et permetainsi aux structures plus faibles hors axe d'émerger et être plus facilementdétectables. Dans ce contexte, la reconstruction d'image est un outilfondamental. Le développement d'interféromètres à haute résolution spectraletelle que AMBER, et bientôt MATISSE et GRAVITY, fait de la reconstruction d'imagepolychromatique une priorité.Cette thèse a comme objectif de développer et d'améliorer des techniques dereconstruction d'image hyperspectrale. Le travail présenté s'articule en deuxparties. En premier, nous discutons le potentiel de l'interférométrie annulantedans le cadre de la résolution du problème inverse. Ce travail repose sur dessimulations numériques et sur l'exploitation de données collectées sur le bancinterférométrique annulant PERSEE. Ensuite, nous avons adapté et développé desméthodes de reconstruction d'images monochromatique et polychromatique. Cestechniques ont été appliquées pour étudier l'environnement circumstellaire dedeux objets évolués, Achernar et Eta Carina, à partir de données PIONIER etAMBER.Ce travail apporte des éléments méthodologiques sur la reconstruction d'image etl'analyse hyperspectrale, ainsi que des études spécifiques sur l'environnementd'Achernar et d'Eta Carina<br>Environment of nearby stars requires instruments with high performances in termsof dynamics and angular resolution. The interferometry offers a solution. Inparticular, in the nulling interferometry, the flux of the star on the axis ofthe interferometer is strongly reduced, allowing to emerge fainter structuresaround it. In this context, the image reconstruction is a fundamental andpowerful tool. The advent of the high spectral resolution interferometers such asAMBER, MATISSE and GRAVITY boost the interest in the polychromatic imagereconstruction, in order to exploit all the available spectral information.The goal of this thesis is to develop and improve monochromatic and hyperspectralimaging techniques. The work here presented has two main parts. First, we discussthe performances of the nulling in the context of the inverse problem solving.This part is based on simulations and data collected on the nulling test benchPERSEE. Second, we adapted and developed monochromatic and hyperspectral imagereconstruction methods. Then, we applied these methods in order to study thecircumstellar environment of two evolved objects, Achernar and Eta Carina, fromPIONIER and AMBER observations.This work provides elements in the field of the image reconstruction forminterferometric observations as well as the specific studies on the environmentof Achernar and Eta Carina
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Mihoubi, Sofiane. "Snapshot multispectral image demosaicing and classification." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I062/document.

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Les caméras multispectrales échantillonnent le spectre du visible et/ou de l'infrarouge selon des bandes spectrales étroites. Parmi les technologies disponibles, Les caméras snapshot équipées d'une mosaïque de filtres acquièrent des images brutes à cadence vidéo. Ces images brutes nécessitent un processus de dématriçage permettant d'estimer l'image multispectrale en pleine définition. Dans ce manuscrit nous examinons les méthodes de dématriçage multispectrale et proposons une nouvelle méthode basée sur l'image panchromatique. De plus, nous mettons en évidence l'influence de l'illumination sur les performances de dématriçage, puis nous proposons des étapes de normalisation rendant ce dernier robuste aux propriétés d'acquisition. Les résultats expérimentaux montrent que notre méthode fournit de meilleurs résultats que les méthodes classiques.Afin d'effectuer une analyse de texture, nous étendons les opérateurs basés sur les motifs binaires locaux aux images de texture multispectrale au détriment d'exigences de mémoire et de calcul accrues. Nous proposons alors de calculer les descripteurs de texture directement à partir d'images brutes, ce qui évite l'étape de dématriçage tout en réduisant la taille du descripteur. Afin d'évaluer la classification sur des images multispectrales, nous avons proposé la première base de données multispectrale de textures proches dans les domaines spectraux du visible et du proche infrarouge. Des expériences approfondies sur cette base montrent que le descripteur proposé a à la fois un coût de calcul réduit et un pouvoir de discrimination élevé en comparaison avec les descripteurs classiques appliqués aux images dématriçées<br>Multispectral cameras sample the visible and/or the infrared spectrum according to narrow spectral bands. Available technologies include snapshot multispectral cameras equipped with filter arrays that acquire raw images at video rate. Raw images require a demosaicing procedure to estimate a multispectral image with full spatio-spectral definition. In this manuscript we review multispectral demosaicing methods and propose a new one based on the pseudo-panchromatic image. We highlight the influence of illumination on demosaicing performances, then we propose pre- and post-processing normalization steps that make demosaicing robust to acquisition properties. Experimental results show that our method provides estimated images of better objective quality than classical ones.Multispectral images can be used for texture classification. To perform texture analysis, we extend local binary pattern operators to multispectral texture images at the expense of increased memory and computation requirements. We propose to compute texture descriptors directly from raw images, which both avoids the demosaicing step and reduces the descriptor size. In order to assess classification on multispectral images we have proposed the first significant multispectral database of close-range textures in the visible and near infrared spectral domains. Extensive experiments on this database show that the proposed descriptor has both reduced computational cost and high discriminating power with regard to classical local binary pattern descriptors applied to demosaiced images
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Hadj-Youcef, Mohamed Elamine. "Spatio spectral reconstruction from low resolution multispectral data : application to the Mid-Infrared instrument of the James Webb Space Telescope." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS326/document.

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Cette thèse traite un problème inverse en astronomie. L’objectif est de reconstruire un objet 2D+λ, ayant une distribution spatiale et spectrale, à partir d’un ensemble de données multispectrales de basse résolution fournies par l’imageur MIRI (Mid-InfraRed Instrument), qui est à bord du prochain télescope spatial James Webb Space Telescope (JWST). Les données multispectrales observées souffrent d’un flou spatial qui dépend de la longueur d’onde. Cet effet est dû à la convolution par la réponse optique (PSF). De plus, les données multi-spectrales souffrent également d’une sévère dégradation spectrale en raison du filtrage spectral et de l’intégration par le détecteur sur de larges bandes. La reconstruction de l’objet original est un problème mal posé en raison du manque important d’informations spectrales dans l’ensemble de données multispectrales. La difficulté se pose alors dans le choix d’une représentation de l’objet permettant la reconstruction de l’information spectrale. Un modèle classique utilisé jusqu’à présent considère une PSF invariante spectralement par bande, ce qui néglige la variation spectrale de la PSF. Cependant, ce modèle simpliste convient que dans le cas d’instrument à une bande spectrale très étroite, ce qui n’est pas le cas pour l’imageur de MIRI. Notre approche consiste à développer une méthode pour l’inversion qui se résume en quatre étapes : (1) concevoir un modèle de l’instrument reproduisant les données multispectrales observées, (2) proposer un modèle adapté pour représenter l’objet à reconstruire, (3) exploiter conjointement l’ensemble des données multispectrales, et enfin (4) développer une méthode de reconstruction basée sur la régularisation en introduisant des priori à la solution. Les résultats de reconstruction d’objets spatio-spectral à partir de neuf images multispectrales simulées de l’imageur de MIRI montrent une augmentation significative des résolutions spatiale et spectrale de l’objet par rapport à des méthodes conventionnelles. L’objet reconstruit montre l’effet de débruitage et de déconvolution des données multispectrales. Nous avons obtenu une erreur relative n’excédant pas 5% à 30 dB et un temps d’exécution de 1 seconde pour l’algorithme de norm-l₂ et 20 secondes avec 50 itérations pour l’algorithme norm-l₂/l₁. C’est 10 fois plus rapide que la solution itérative calculée par l’algorithme de gradient conjugué<br>This thesis deals with an inverse problem in astronomy. The objective is to reconstruct a spatio-spectral object, having spatial and spectral distributions, from a set of low-resolution multispectral data taken by the imager MIRI (Mid-InfraRed Instrument), which is on board the next space telescope James Webb Space Telescope (JWST). The observed multispectral data suffers from a spatial blur that varies according to the wavelength due to the spatial convolution with a shift-variant optical response (PSF). In addition the multispectral data also suffers from severe spectral degradations because of the spectral filtering and the integration by the detector over broad bands. The reconstruction of the original object is an ill-posed problem because of the severe lack of spectral information in the multispectral dataset. The difficulty then arises in choosing a representation of the object that allows the reconstruction of this spectral information. A common model used so far considers a spectral shift-invariant PSF per band, which neglects the spectral variation of the PSF. This simplistic model is only suitable for instruments with a narrow spectral band, which is not the case for the imager of MIRI. Our approach consists of developing an inverse problem framework that is summarized in four steps: (1) designing an instrument model that reproduces the observed multispectral data, (2) proposing an adapted model to represent the sought object, (3) exploiting all multispectral dataset jointly, and finally (4) developing a reconstruction method based on regularization methods by enforcing prior information to the solution. The overall reconstruction results obtained on simulated data of the JWST/MIRI imager show a significant increase of spatial and spectral resolutions of the reconstructed object compared to conventional methods. The reconstructed object shows a clear denoising and deconvolution of the multispectral data. We obtained a relative error below 5% at 30 dB, and an execution time of 1 second for the l₂-norm algorithm and 20 seconds (with 50 iterations) for the l₂/l₁-norm algorithm. This is 10 times faster than the iterative solution computed by conjugate gradients
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Mugnier, Laurent. "Problèmes inverses en Haute Résolution Angulaire." Habilitation à diriger des recherches, Université Paris-Diderot - Paris VII, 2011. http://tel.archives-ouvertes.fr/tel-00654835.

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Les travaux exposés portent sur les techniques d'imagerie optique à haute résolution et plus particulièrement sur les méthodes, dites d'inversion, de traitement des données associées à ces techniques. Ils se situent donc à la croisée des chemins entre l'imagerie optique et le traitement du signal et des images. Ces travaux sont appliqués à l'astronomie depuis le sol ou l'espace, l'observation de la Terre, et l'imagerie de la rétine. Une partie introductive est dédiée au rappel de caractéristiques importantes de l'inversion de données et d'éléments essentiels sur la formation d'image (diffraction, turbulence, techniques d'imagerie) et sur la mesure des aberrations (analyse de front d'onde). La première partie des travaux exposés porte sur l'étalonnage d'instrument, c'est-à-dire l'estimation d'aberrations instrumentales ou turbulentes. Ils concernent essentiellement la technique de diversité de phase : travaux méthodologiques, travaux algorithmiques, et extensions à l'imagerie à haute dynamique en vue de la détection et la caractérisation d'exoplanètes. Ces travaux comprennent également des développements qui n'utilisent qu'une seule image au voisinage du plan focal, dans des cas particuliers présentant un intérêt pratique avéré. La seconde partie des travaux porte sur le développement de méthodes de traitement (recalage, restauration et reconstruction, détection) pour l'imagerie à haute résolution. Ces développements ont été menés pour des modalités d'imagerie très diverses : imagerie corrigée ou non par optique adaptative (OA), mono-télescope ou interférométrique, pour l'observation de l'espace ; imagerie coronographique d'exoplanètes par OA depuis le sol ou par interférométrie depuis l'espace ; et imagerie 2D ou 3D de la rétine humaine. Enfin, une dernière partie présente des perspectives de recherches.
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Book chapters on the topic "Hyperspectral image reconstruction"

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Zhu, Wei, Zuoqiang Shi, and Stanley Osher. "Low Dimensional Manifold Model in Hyperspectral Image Reconstruction." In Hyperspectral Image Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38617-7_10.

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Wang, Jiamian, Yulun Zhang, Xin Yuan, Ziyi Meng, and Zhiqiang Tao. "Modeling Mask Uncertainty in Hyperspectral Image Reconstruction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19800-7_7.

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Xu, Meng, Mingying Lin, Qi Ren, and Sen Jia. "SSTHyper: Sparse Spectral Transformer for Hyperspectral Image Reconstruction." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-96-0911-6_9.

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Pande, Shivam, Biplab Banerjee, and Aleksandra Pižurica. "Class Reconstruction Driven Adversarial Domain Adaptation for Hyperspectral Image Classification." In Pattern Recognition and Image Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31332-6_41.

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Cai, Yuanhao, Jing Lin, Xiaowan Hu, et al. "Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19790-1_41.

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Yamawaki, Kazuhiro, and Xian-Hua Han. "Lightweight Hyperspectral Image Reconstruction Network with Deep Feature Hallucination." In Computer Vision – ACCV 2022 Workshops. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27066-6_12.

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Wang, Chao, Shuai Gao, Xinming Sun, Shiji Liu, and Wanli Lv. "Dual Cross Fusion Deep-Unfolding Transformer for Hyperspectral Image Reconstruction." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6579-2_18.

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Fu, Ying, and Yingkai Zhang. "Reinforcement Learning Based Plug-and-Play Method for Hyperspectral Image Reconstruction." In Artificial Intelligence. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20497-5_38.

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Wang, Qian, and Zhao Chen. "A Deep Wavelet Network for High-Resolution Microscopy Hyperspectral Image Reconstruction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25082-8_44.

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Luo, Haobin, Guowei Su, Yi Wang, Jiajia Zhang, and Luobing Dong. "Hyperspectral Image Reconstruction for SD-CASSI Systems Based on Residual Attention Network." In Algorithmic Aspects in Information and Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16081-3_41.

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Conference papers on the topic "Hyperspectral image reconstruction"

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Ma, Ye, Songnan Lin, and Bihan Wen. "Hyperspectral Image Reconstruction with Unseen Material Detection." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888903.

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Yang, Jincheng, Lishun Wang, Miao Cao, Huan Wang, Yinping Zhao, and Xin Yuan. "Coarse-Fine Spectral-Aware Deformable Convolution for Hyperspectral Image Reconstruction." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647725.

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Han, Xian-Hua, and Jian Wang. "Multi-Degradation Oriented Deep Unfolding Model for Hyperspectral Image Reconstruction." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890865.

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Tian, Fenggang, and Long Ma. "A Hybrid CNN-Transformer Model for Efficient Hyperspectral Image Reconstruction." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796152.

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Yao, Zhiyang, Shuyang Liu, Xiaoyun Yuan, and Lu Fang. "SPECAT: SPatial-spEctral Cumulative-Attention Transformer for High-Resolution Hyperspectral Image Reconstruction." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02397.

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Pruitt, Kelden, Hemanth Pasupuleti, James Yu, Weston DeAtley, and Baowei Fei. "Masked image modeling in medical hyperspectral imaging: reconstruction evaluation and downstream tasks." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3048802.

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Jin, Yantao, Zhenming Yu, Jiayu Di, et al. "Hyperspectral Image Reconstruction Using Spatial Enhancement Neural Networks with Hybrid Prior Strategy." In 2024 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC). IEEE, 2024. https://doi.org/10.1109/acp/ipoc63121.2024.10809508.

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He, Zijun, Lishun Wang, Ziyi Meng, and Xin Yuan. "Self-supervised Learning with Spectral Low-Rank Prior for Hyperspectral Image Reconstruction." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00885.

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Zhang, Zheng, Tiecheng Song, Linnan Xie, Yinghao Jiu, and Huaiyi Sun. "Hyperspectral image reconstruction using a lightweight frequency-enhanced network with spectral-spatial dual priors." In Sixteenth International Conference on Digital Image Processing (ICDIP 2024), edited by Zhaohui Wang, Jindong Tian, and Mrinal Mandal. SPIE, 2024. http://dx.doi.org/10.1117/12.3037379.

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Sain, Shikha, and Monika Saxena. "Hyperspectral Image Reconstruction in Remote Sensing: LaplaceGAN Synthesis Coupled with VGG-UNet Classification." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687991.

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