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Journal articles on the topic 'Multi-dimensional image'

Author: Grafiati

Published: 10 January 2023

Last updated: 28 January 2023

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1

Legland, David, and Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab." F1000Research 10 (April 30, 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

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Lukac, Rastislav, Phillip A. Laplante, and Konstantinos N. Plataniotis. "Multi-dimensional image processing." Real-Time Imaging 11, no. 5-6 (October 2005): 355–57. http://dx.doi.org/10.1016/j.rti.2005.08.002.

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Park, Jong-Il, and Seiki Inoue. "Three-Dimensional Image Information. Image-Based Rendering from Multi-View Images." Journal of the Institute of Image Information and Television Engineers 52, no. 3 (1998): 371–76. http://dx.doi.org/10.3169/itej.52.371.

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4

Wang, Ke Feng, Su Zhuang, and Xiao Rong Zhao. "JPEG Image Encryption Algorithm Based on Three-Dimensional Multi-Chaotic System." Applied Mechanics and Materials 734 (February 2015): 554–57. http://dx.doi.org/10.4028/www.scientific.net/amm.734.554.

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The paper decribes the JPEG image encryption algorithm based on three-dimensional multi-chaotic system. The algorithm is designed to segment the image into three-demensional matrix system with a variety of chaoic encryption system. The images are scrambled and transformed in the three-dimensional space, and then by the three-dimensional chaotic sequence from multi-chaotic system, they are respectively transformed as airspace tricolor per-pixel alternative images. Reaearch results show that the algorithm has good confusion and diffusion properties of pixels and a powerful key space of greater resistance. The encrypted image pixels are distrubuted randomly and evenly with adjacent pixels of zero correlation properties, which proves the proposed scheme has a high security.

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Ibaroudene, Djaffer, and Raj Acharya. "Linear hypertree for multi-dimensional image representation." Information Sciences 68, no. 1-2 (February 1993): 123–54. http://dx.doi.org/10.1016/0020-0255(93)90025-h.

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6

Martin, Ingrid M., and Sevgin Eroglu. "Measuring a multi-dimensional construct: Country image." Journal of Business Research 28, no. 3 (November 1993): 191–210. http://dx.doi.org/10.1016/0148-2963(93)90047-s.

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7

de Oliveira, H. M., V. V. Vermehren, and R. J. Cintra. "Multi-dimensional wavelets for scalable image decomposition: Orbital wavelets." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 05 (June 15, 2020): 2050038. http://dx.doi.org/10.1142/s0219691320500381.

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Wavelets are closely related to Schrödinger’s wave functions and the interpretation of Born. Similar to the appearance of atomic orbital, it is proposed to combine anti-symmetric wavelets into orbital wavelets. The proposed approach allows the increase of the dimension of wavelets through this process. New orbital 2D-wavelets are introduced for the decomposition of still images, showing that it is possible to perform an analysis simultaneously in two distinct scales. An example of such an image analysis is shown.

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Yu, Xia, Lin Bo, and Chen Xin. "Low light combining multiscale deep learning networks and image enhancement algorithm." Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies 2, no. 4 (November 28, 2022): 0215–32. http://dx.doi.org/10.47813/2782-2818-2022-2-4-0215-0232.

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Aiming at the lack of reference images for low-light enhancement tasks and the problems of color distortion, texture loss, blurred details, and difficulty in obtaining ground-truth images in existing algorithms, this paper proposes a multi-scale weighted feature low-light based on Retinex theory and attention mechanism. An image enhancement algorithm is proposed. The algorithm performs multi-scale feature extraction on low-light images through the feature extraction module based on the Unet architecture, generates a high-dimensional multi-scale feature map, and establishes an attention mechanism module to highlight the feature information of different scales that are beneficial to the enhanced image, and obtain a weighted image. High-dimensional feature map, the final reflection estimation module uses Retinex theory to build a network model, and generates the final enhanced image through the high-dimensional feature map. An end-to-end network architecture is designed and a set of self-regular loss functions are used to constrain the network model, which gets rid of the constraints of reference images and realizes unsupervised learning. The final experimental results show that the algorithm in this paper maintains high image details and textures while enhancing the contrast and clarity of the image, has good visual effects, can effectively enhance low-light images, and greatly improves the visual quality. Compared with other enhanced algorithms, the objective indicators PSNR and SSIM have been improved.

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Patro, K. Abhimanyu Kumar, and Bibhudendra Acharya. "A novel multi-dimensional multiple image encryption technique." Multimedia Tools and Applications 79, no. 19-20 (January 24, 2020): 12959–94. http://dx.doi.org/10.1007/s11042-019-08470-8.

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10

Wang, Cailing, Hongwei Wang, Yinyong Zhang, Jia Wen, and Fan Yang. "High Dimensional Feature for Hyperspectral Image Classification." MATEC Web of Conferences 246 (2018): 03041. http://dx.doi.org/10.1051/matecconf/201824603041.

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Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage. In this paper, we study the performance of a high-dimensional feature by texture feature. The texture feature based on multi-local binary pattern descriptor, can achieve significant improvements over both its tradition version and the one we proposed in our previous work. We also make the high-dimensional feature practical, we employ the PCA method for dimension reduction and support vector machine for hyperspectral image classification. The two real hyperspectral image datasets are employed. Our experimental results with real hyperspectral images indicate that the high dimensional feature can enhance the classification accuracy than some low dimensional.

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11

Summers, R., G. Martins, and J. Morrill. "Multi-Dimensional Microscopic Analysis of Sea Urchin Development." Microscopy and Microanalysis 5, S2 (August 1999): 1068–69. http://dx.doi.org/10.1017/s1431927600018663.

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Several modalities of microscopic analysis have been employed in the investigation of development in the embryos of the sea urchins Strongylocentrotus purpuratusand Lytechinusvariegatus. These include scanning electron microscopy, confocal optical microscopy and multiphoton laser scanning microscopy. These techniques all provide information on embryonic morphogenesis in three-dimensions and may be of use in the analysis of other developing organisms.Scanning Electron Microscopy. Scanning electron microscopy bridges the gap between light and transmission electron microscopy. Operation in the secondary electron image mode with stereo-pair images combines great depth of field in focus from 10-50,000x with true 3D images of topographical features from whole embryos to macromolecules. The SEM's backscatter electron imaging mode allows one to see and map spatial geographies of colloidal gold labeled probes (e.g. antibodies, lectins) that bind to exposed surfaces of embryos, cells and extracellular matrices.

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Ye, L., M. Peng, K. Di, B. Liu, and Y. Wang. "LUNAR TERRAIN RECONSTRUCTION FROM MULTI-VIEW LROC NAC IMAGES BASED ON SEMI-GLOBAL MATCHING IN OBJECT SPACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1177–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1177-2020.

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Abstract. Most of the lunar surface area has been observed from different viewing conditions thanks to the on-orbit work of lunar orbiters, a large amount of images are available for photogrammetric three-dimensional mapping, which is an important issue for lunar exploration. Theoretically, multi-view images contain more information than a single stereo pair and can get better 3D mapping results. In this paper, the semi-global matching method is applied to the object space, and the steps of cost calculation, cost aggregation, and elevation calculation are performed to obtain the three-dimensional coordinates directly. Compared with the traditional image-based semi-global matching method, the object-based semi-global method is more easily extended to multi-view images, which is beneficial for applying multi-view image information. In addition, it does not require steps such as stereo rectification and forward intersection, that is, the overall pipeline is more elegant. Using the LRO NAC images covering Apollo 11 landing area as the experimental data, the result shows that the object-based semi-global matching is competent for the multi-view image matching and the multi-view image result achieves higher accuracy and more details than the single stereo pair. Furthermore, the experimental results of Zhinyu crater data show that this method can also alleviate the uncertainty of the lunar orbiter's positioning to some extent.

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Lu, Liang, Hongbao Zhu, Junyu Dong, Yakun Ju, and Huiyu Zhou. "Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo." Sensors 21, no. 6 (March 18, 2021): 2131. http://dx.doi.org/10.3390/s21062131.

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This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods.

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14

Shen, Jiaqi, Fangfang Huang, and Myers Ulrich. "Evaluation and Analysis of Cardiovascular Function in Intensive Care Unit Patients by Ultrasound Image Segmentation Based on Deep Learning." Journal of Medical Imaging and Health Informatics 10, no. 8 (August 1, 2020): 1892–98. http://dx.doi.org/10.1166/jmihi.2020.3119.

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Many studies have shown that cardiovascular disease has become one of the major diseases leading to death in the world. Therefore, it is a very meaningful topic to use image segmentation technology to segment blood vessels for clinical application. In order to automatically extract the features of blood vessel images in the process of segmentation, the deep learning algorithm is combined with image segmentation technology to segment the nerve cell membrane and carotid artery images of ICU patients, and to segment the blood vessel images from a multi-dimensional perspective. The relevant data are collected to observe the effect of this model. The results show that the three-dimensional multi-scale linear filter has a good effect on carotid artery segmentation in the image segmentation of nerve cell membranes and carotid artery. When analyzing the accuracy of vascular image segmentation from network parameters and training parameters, it is found that the accuracy of the threedimensional multi-scale linear filter can reach about 85%. Therefore, it can be found that the combination of deep learning algorithm and image segmentation technology has a good segmentation effect, and the segmentation accuracy is also high. The experiment achieves the desired effect, which provides experimental basis for the clinical application of the vascular image segmentation technology.

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15

Kim, Dong Moon. "Construction of Multi-Dimensional Ortho-Images with a Digital Camera and the Multi-Image Connection Method." Journal of Digital Convergence 12, no. 8 (August 28, 2014): 295–302. http://dx.doi.org/10.14400/jdc.2014.12.8.295.

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16

Zhu, Min, Rongfu Zhang, Pei Ma, Xuedian Zhang, and Qi Guo. "Three-dimensional Reconstruction of Microscopic Image Based on Multi-ST Algorithm." Journal of Imaging Science and Technology 64, no. 2 (March 1, 2020): 20506–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.2.020506.

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Abstract Three-dimensional (3D) reconstruction is extensively used in microscopic applications. Reducing excessive error points and achieving accurate matching of weak texture regions have been the classical challenges for 3D microscopic vision. A Multi-ST algorithm was proposed to improve matching accuracy. The process is performed in two main stages: scaled microscopic images and regularized cost aggregation. First, microscopic image pairs with different scales were extracted according to the Gaussian pyramid criterion. Second, a novel cost aggregation approach based on the regularized multi-scale model was implemented into all scales to obtain the final cost. To evaluate the performances of the proposed Multi-ST algorithm and compare different algorithms, seven groups of images from the Middlebury dataset and four groups of experimental images obtained by a binocular microscopic system were analyzed. Disparity maps and reconstruction maps generated by the proposed approach contained more information and fewer outliers or artifacts. Furthermore, 3D reconstruction of the plug gauges using the Multi-ST algorithm showed that the error was less than 0.025 mm.

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17

Li, Xiao Peng, and Lan Zhen Chen. "Research on the Application of BP Neural Networks in 3D Reconstruction Noise Filter." Advanced Materials Research 998-999 (July 2014): 911–14. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.911.

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At present, in the most of the digital image system, the input image is used to freeze the multi-dimensional image scanning way again into a one dimensional signal, then carries on the processing, storage, transmission and processing. Finally tend to form multi-dimensional image signal, and the image noise will also be decomposed and compounded. Electrical systems and outside influences in these procedures will enable precise analysis of complexity of image noise. According to off-line learning method of neural networks, this paper focus on the noise filter in the 3Dreconstruction process in order to make the image clearer.

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18

Lee, Jeoung-Hak, Seung-jae Lim, Sung-Yong Kim, and ki-woong Kim. "Professional volleyball club emblem image for Multi-Dimensional Scaling." Korean Journal of Sport Management 22, no. 2 (April 30, 2017): 67–78. http://dx.doi.org/10.31308/kssm.22.2.5.

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19

Carazo, J. M., E. H. K. Stelzer, A. Engel, I. Fita, C. Henn, J. Machtynger, P. McNeil, et al. "Organising multi-dimensional biological image information: The BioImage Database." Nucleic Acids Research 27, no. 1 (January 1, 1999): 280–83. http://dx.doi.org/10.1093/nar/27.1.280.

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20

Burel, Jean-Marie, Sébastien Besson, Colin Blackburn, Mark Carroll, Richard K. Ferguson, Helen Flynn, Kenneth Gillen, et al. "Publishing and sharing multi-dimensional image data with OMERO." Mammalian Genome 26, no. 9-10 (July 30, 2015): 441–47. http://dx.doi.org/10.1007/s00335-015-9587-6.

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21

Gilles, Jean-François, and Thomas Boudier. "TAPAS: Towards Automated Processing and Analysis of multi-dimensional bioimage data." F1000Research 9 (July 2, 2021): 1278. http://dx.doi.org/10.12688/f1000research.26977.2.

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Modern microscopy is based on reproducible quantitative analysis, image data should be batch-processed by a standardized system that can be shared and easily reused by others. Furthermore, such system should require none or minimal programming from the users. We developed TAPAS (Towards an Automated Processing and Analysis System). The goal is to design an easy system for describing and exchanging processing workflows. The protocols are simple text files comprising a linear list of commands used to process and analyse the images. An extensive set of 60 modules is already available, mostly based on the tools proposed in the 3D ImageJ Suite. We propose a wizard, called TAPAS menu, to help the user design the protocol by listing the available modules and the parameters associated. Most modules will have default parameters values for most common tasks. Once the user has designed the protocol, he/she can apply the protocol to a set of images, that can be either stored locally or on a OMERO database. An extensive documentation including the list of modules, various tutorials and link to the source code is available at https://imagej.net/TAPAS.

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Gilles, Jean-François, and Thomas Boudier. "TAPAS: Towards Automated Processing and Analysis of multi-dimensional bioimage data." F1000Research 9 (October 28, 2020): 1278. http://dx.doi.org/10.12688/f1000research.26977.1.

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Modern microscopy is based on reproducible quantitative analysis, image data should be batch-processed by a standardized system that can be shared and easily reused by others. Furthermore such system should require none or minimal programming from the users. We developed TAPAS (Towards an Automated Processing and Analysis System). The goal is to design an easy system for describing and exchanging processing workflows. The protocols are simple text files comprising a linear list of commands used to process and analyse the images. An extensive set of 60 modules is already available, mostly based on the tools proposed in the 3D ImageJ Suite. We propose a wizard, called TAPAS menu, to help the user design her protocol by listing the available modules and the parameters associated. Most modules will have default parameters values for most common tasks. Once the user has designed her protocol, she can apply the protocol to a set of images, that can be either stored locally or on a OMERO database. An extensive documentation including the list of modules, various tutorials and link to the source code is available at https://imagej.net/TAPAS.

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23

Zhang, Haiqing, and Jun Han. "Mathematical models for information classification and recognition of multi-target optical remote sensing images." Open Physics 18, no. 1 (December 10, 2020): 951–60. http://dx.doi.org/10.1515/phys-2020-0123.

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Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

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24

Wei, Jingjin, Miao Zhang, and Xiaojun Tong. "Multi-Image Compression–Encryption Algorithm Based on Compressed Sensing and Optical Encryption." Entropy 24, no. 6 (June 2, 2022): 784. http://dx.doi.org/10.3390/e24060784.

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In order to achieve large-capacity, fast and secure image transmission, a multi-image compression–encryption algorithm based on two-dimensional compressed sensing (2D CS) and optical encryption is proposed in this paper. Firstly, the paper uses compressed sensing to compress and encrypt multiple images simultaneously, and design a new structured measurement matrix. Subsequently, double random phase encoding based on the multi-parameter fractional quaternion Fourier transform is used to encrypt the multiple images for secondary encryption, which improves the security performance of the images. Moreover, a fractional-order chaotic system with more complex chaotic behavior is constructed for image compression and encryption. Experimental results show that the algorithm has strong robustness and security.

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Wang, Jing Hui, and Shu Gang Tang. "Image Blind Separation Based on Adaptive Multi-Resolution Independent Component Analysis." Advanced Materials Research 586 (November 2012): 365–69. http://dx.doi.org/10.4028/www.scientific.net/amr.586.365.

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In this paper, a novel image blind separation using adaptive multi-resolution independent component analysis is presented.This method separates mixed images based on quadratic function. The quadratic function can be interpreted as the time-frequency function or time-scale function, or other. According to the signal characteristics, we can choose the frequency resolution or scale resolution. The argorithm extends the separate technology from one dimensional domain to two dimensional domain,and it’s implement by adaptive procedure. The experimental result showed the method can be effective separation of mixed images. And it shows that the method is feasible.

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Tsai, Chia-Ming, Yi-Horng Lai, Yung-Da Sun, Yu-Jen Chung, and Jau-Woei Perng. "Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning." Sensors 21, no. 3 (January 28, 2021): 884. http://dx.doi.org/10.3390/s21030884.

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Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

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Krainy, Vladimir I., Aleksandr N. Semenov, and Anastasiya A. Sergeeva. "Reconstruction of radio images from a multifrequency multistatic radio hologram using the nonequidistant FFT method." Radioelectronics. Nanosystems. Information Technologies. 14, no. 1 (April 12, 2022): 21–26. http://dx.doi.org/10.17725/rensit.2022.14.021.

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The article considers the transformation of the back-projection method (focusing) for recovering geometry from the knowledge of the scattered field received with a multi-static radar system (multi-static radio hologram). It is shown that it can be expressed as a two-dimensional non-equidistant Discrete Fourier Transform (NDFT). An example of focusing a multi-frequency multi-static radio hologram of a flat multipoint object is given, and a reconstructed image is obtained based on the back-projection algorithm and the NDFT algorithms. A modification of NDFT based on the fast Gaussian gridding of non-uniform nodes (NuFFT), implemented in MATLAB, is used to speed up image reconstruction. A numerical experiment indicates similar reconstructed images obtained by the backpropagation method and with the two-dimensional NuFFT method. However, the NuFFT method was 740 times faster. The results show a significant reduction.

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Galshetwar, G. M., L. M. Waghmare, A. B. Gonde, and S. Murala. "Multi-dimensional multi-directional mask maximum edge pattern for bio-medical image retrieval." International Journal of Multimedia Information Retrieval 7, no. 4 (June 19, 2018): 231–39. http://dx.doi.org/10.1007/s13735-018-0156-0.

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Mahesh R N, Uma, and Anith Nelleri. "Multi-Class Classification and Multi-Output Regression of Three-Dimensional Objects Using Artificial Intelligence Applied to Digital Holographic Information." Sensors 23, no. 3 (January 17, 2023): 1095. http://dx.doi.org/10.3390/s23031095.

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Digital holographically sensed 3D data processing, which is useful for AI-based vision, is demonstrated. Three prominent methods of learning from datasets such as sensed holograms, computationally retrieved intensity and phase from holograms forming concatenated intensity–phase (whole information) images, and phase-only images (depth information) were utilized for the proposed multi-class classification and multi-output regression tasks of the chosen 3D objects in supervised learning. Each dataset comprised 2268 images obtained from the chosen eighteen 3D objects. The efficacy of our approaches was validated on experimentally generated digital holographic data then further quantified and compared using specific evaluation matrices. The machine learning classifiers had better AUC values for different classes on the holograms and whole information datasets compared to the CNN, whereas the CNN had a better performance on the phase-only image dataset compared to these classifiers. The MLP regressor was found to have a stable prediction in the test and validation sets with a fixed EV regression score of 0.00 compared to the CNN, the other regressors for holograms, and the phase-only image datasets, whereas the RF regressor showed a better performance in the validation set for the whole information dataset with a fixed EV regression score of 0.01 compared to the CNN and other regressors.

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Islam, Kh Tohidul, Sudanthi Wijewickrema, and Stephen O’Leary. "A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images." Sensors 22, no. 2 (January 11, 2022): 523. http://dx.doi.org/10.3390/s22020523.

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Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods.

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Jung, Hyungjoo, Hyunsung Jang, Namkoo Ha, and Kwanghoon Sohn. "Deep Low-Contrast Image Enhancement using Structure Tensor Representation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1725–33. http://dx.doi.org/10.1609/aaai.v35i2.16266.

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We present a new deep learning framework for low-contrast image enhancement, which trains the network using the multi-exposure sequences rather than explicit ground-truth images. The purpose of our method is to enhance a low-contrast image so as to contain abundant details in various exposure levels. To realize this, we propose to design the loss function using the structure tensor representation, which has been widely used as high-dimensional image contrast. Our loss function penalizes the difference of the structure tensor between the network output and the multi-exposure images in a multi-scale manner. Eventually, the network trained by the loss function produces a high-quality image approximating the overall contrast of the sequence. We provide in-depth analysis on our method and comparison with conventional loss functions. Quantitative and qualitative evaluations demonstrate that the proposed method outperforms the existing state-of-the-art approaches in various benchmarks.

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Colson, Alicia, and Ross Parry. "Shifting perspectives: method, media and the complex image." History and Computing 10, no. 1-3 (October 1998): 100–108. http://dx.doi.org/10.3366/hac.1998.10.1-3.100.

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This article argues that the analysis of a threedimensional image demanded a three-dimensional approach. The authors realise that discussions of images and image processing inveterately conceptualise representation as being flat, static, and finite. The authors recognise the need for a fresh acuteness to three-dimensionality as a meaningful – although problematic – element of visual sources. Two dramatically different examples are used to expose the shortcomings of an ingrained two-dimensional approach and to facilitate a demonstration of how modern (digital) techniques could sanction new historical/anthropological perspectives on subjects that have become all too familiar. Each example could not be more different in their temporal and geographical location, their cultural resonance, and their historiography. However, in both these visual spectacles meaning is polysemic. It is dependent upon the viewer's spatial relationship to the artifice as well as the spirito-intellectual viewer within the community. The authors postulate that the multi- faceted and multi-layered arrangement of meaning in a complex image could be assessed by working beyond the limitations of the two-dimensional methodological paradigm and by using methods and media that accommodated this type of interconnectivity and representation.

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Hu, Huiran, and Aiguo Song. "Haptic Texture Rendering of 2D Image Based on Adaptive Fractional Differential Method." Applied Sciences 12, no. 23 (December 2, 2022): 12346. http://dx.doi.org/10.3390/app122312346.

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The fractional differential algorithm has a good effect on extracting image textures, but it is usually necessary to select an appropriate fractional differential order for textures of different scales, so we propose a novel approach for haptic texture rendering of two-dimensional (2D) images by using an adaptive fractional differential method. According to the fractional differential operator defined by the Grünvald–Letnikov derivative (G–L) and combined with the characteristics of human vision, we propose an adaptive fractional differential method based on the composite sub-band gradient vector of the sub-image obtained by wavelet decomposition of the image texture. We apply these extraction results to the haptic display system to reconstruct the three-dimensional (3D) texture force filed to render the texture surface of two-dimensional (2D) images. Based on this approach, we carry out the quantitative analysis of the haptic texture rendering of 2D images by using the multi-scale structural similarity (MS-SSIM) and image information entropy. Experimental results show that this method can extract the texture features well and achieve the best texture force file for 2D images.

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Li, Wei, and Xin Yu Zhang. "Application of Digital Image Processing Software in Ceramic Design." Applied Mechanics and Materials 543-547 (March 2014): 2435–39. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2435.

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From rest to exercise and from two-dimensional to three-dimensional, digital image processing software can be multi-angle, multi-dimension, multi-direction infiltration for the various fields of design, which has wide applications in every corner of digital media. Firstly, the digital image processing software can analyze the common digital processing system model. On the basis of the principle of RGB color histogram, it also can analyze its application in the ceramic design. According to the set of image transformation principle, the analysis of the geometric transformation plays an important role in in ceramic set, to provide the theoretical basis for the digital image processing software to a certain extent.

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Wang, Yunxi. "On the Application of Dynamic Logos in Brand Image Design." Learning & Education 9, no. 1 (March 15, 2020): 13. http://dx.doi.org/10.18282/l-e.v9i1.868.

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<p>Market competition is increasingly fierce in the new era. Enterprises must focus on establishing their good brand images in order to occupy a favorable position in the competition. The effective application of dynamic logos in brand image design can make logos develop towards three-dimensional or even multi-dimensional space, enriching and diversifying brand logos through dynamic graphics, personalized voice, rich colors, <em>etc.</em></p>

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Fournel, Joris, Axel Bartoli, David Bendahan, Maxime Guye, Monique Bernard, Elisa Rauseo, Mohammed Y. Khanji, Steffen E. Petersen, Alexis Jacquier, and Badih Ghattas. "Medical image segmentation automatic quality control: A multi-dimensional approach." Medical Image Analysis 74 (December 2021): 102213. http://dx.doi.org/10.1016/j.media.2021.102213.

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Swoger, Jim, Peter Verveer, Klaus Greger, Jan Huisken, and Ernst H. K. Stelzer. "Multi-view image fusion improves resolution in three-dimensional microscopy." Optics Express 15, no. 13 (June 13, 2007): 8029. http://dx.doi.org/10.1364/oe.15.008029.

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SUGISHITA, Tomotaka, Terumoto KOMORI, Yoshihiko NOMURA, and Norihiko KATO. "Image Segmentation Using Multi Dimensional Co-occurrence Matrix (3rd Report)." Proceedings of the JSME annual meeting 2002.7 (2002): 249–50. http://dx.doi.org/10.1299/jsmemecjo.2002.7.0_249.

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HIRATA, DAISUKE. "A nonlocal mean curvature flow for multi-dimensional image processing." European Journal of Applied Mathematics 16, no. 1 (March 23, 2005): 21–36. http://dx.doi.org/10.1017/s0956792505005863.

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Rubio-Guivernau, Jose L., Vasily Gurchenkov, Miguel A. Luengo-Oroz, Louise Duloquin, Paul Bourgine, Andres Santos, Nadine Peyrieras, and Maria J. Ledesma-Carbayo. "Wavelet-based image fusion in multi-view three-dimensional microscopy." Bioinformatics 28, no. 2 (November 9, 2011): 238–45. http://dx.doi.org/10.1093/bioinformatics/btr609.

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Georgiev, T. B. "Multi-Parametric Iris-Photometry." Symposium - International Astronomical Union 161 (1994): 311. http://dx.doi.org/10.1017/s0074180900047550.

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Usually, the reduction of iris-measurements d of stellar photographic images to magnitudes m is performed with one-dimensional regression m = f(d), obtained using standard stars (Burkhead & Seeds 1971). The derivation of accurate magnitudes is difficult due to many factors (Butler 1972). Considering that the star image shape contains information on the sensitivity of the plate and the color of the star, Vladimirov (1978) recommended multi-parametric determination of the magnitudes, using a system of isodense bounded areas of the stellar images. Here this approach is implemented using software that performs a general multi-parametric method (MPM) of iris-photometry.

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Lewitt, R. M., G. Muehllehner, and J. S. Karp. "Three-dimensional image reconstruction for PET by multi-slice rebinning and axial image filtering." Physics in Medicine and Biology 39, no. 3 (March 1, 1994): 321–39. http://dx.doi.org/10.1088/0031-9155/39/3/002.

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BEFU, Shinobu, Hitoshi TSUNASHIMA, Ayuta YAMADA, and Yoshinori ARAI. "A study on Three-dimensional Image Processing Method for 3DX Multi Image Micro CT." Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2002.14 (2002): 217–18. http://dx.doi.org/10.1299/jsmebio.2002.14.217.

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Yang, You, Xu Wang, Tao Guan, Jialie Shen, and Li Yu. "A multi-dimensional image quality prediction model for user-generated images in social networks." Information Sciences 281 (October 2014): 601–10. http://dx.doi.org/10.1016/j.ins.2014.03.016.

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Priego, Blanca, and Richard J. Duro. "An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images." Sensors 19, no. 13 (June 29, 2019): 2887. http://dx.doi.org/10.3390/s19132887.

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This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging.

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Mu, Guo, and Liu. "A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification." Remote Sensing 12, no. 1 (January 1, 2020): 125. http://dx.doi.org/10.3390/rs12010125.

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Extracting spatial and spectral features through deep neural networks has become an effective means of classification of hyperspectral images. However, most networks rarely consider the extraction of multi-scale spatial features and cannot fully integrate spatial and spectral features. In order to solve these problems, this paper proposes a multi-scale and multi-level spectral-spatial feature fusion network (MSSN) for hyperspectral image classification. The network uses the original 3D cube as input data and does not need to use feature engineering. In the MSSN, using different scale neighborhood blocks as the input of the network, the spectral-spatial features of different scales can be effectively extracted. The proposed 3D–2D alternating residual block combines the spectral features extracted by the three-dimensional convolutional neural network (3D-CNN) with the spatial features extracted by the two-dimensional convolutional neural network (2D-CNN). It not only achieves the fusion of spectral features and spatial features but also achieves the fusion of high-level features and low-level features. Experimental results on four hyperspectral datasets show that this method is superior to several state-of-the-art classification methods for hyperspectral images.

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Zhao, Weiming. "Application of Automatic Analysis of Image Data Based on KD-Tree in Ray Tracing Technology." MATEC Web of Conferences 365 (2022): 01028. http://dx.doi.org/10.1051/matecconf/202236501028.

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In the field of computer graphics, ray tracing technology is widely used to generate high-quality "photo-level" images. The automatic analysis of KD-Tree image data is the data structure of dividing K-dimensional database space, and it has important application in the fast search of multi-dimensional space feature vector. Based on the automatic analysis of KD-Tree image data, the reasonable and effective application in ray tracing technology can make the verisimilitude of graphics reach the leap-type change, and realize the ideal application effect.

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WU, XIAODONG, DANNY Z. CHEN, KANG LI, and MILAN SONKA. "THE LAYERED NET SURFACE PROBLEMS IN DISCRETE GEOMETRY AND MEDICAL IMAGE SEGMENTATION." International Journal of Computational Geometry & Applications 17, no. 03 (June 2007): 261–96. http://dx.doi.org/10.1142/s0218195907002331.

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Efficient detection of multiple inter-related surfaces representing the boundaries of objects of interest in d-D images (d ≥ 3) is important and remains challenging in many medical image analysis applications. In this paper, we study several layered net surface (LNS) problems captured by an interesting type of geometric graphs called ordered multi-column graphs in the d-D discrete space (d ≥ 3 is any constant integer). The LNS problems model the simultaneous detection of multiple mutually related surfaces in three or higher dimensional medical images. Although we prove that the d-D LNS problem (d ≥ 3) on a general ordered multi-column graph is NP-hard, the (special) ordered multi-column graphs that model medical image segmentation have the self-closure structures and thus admit polynomial time exact algorithms for solving the LNS problems. Our techniques also solve the related net surface volume (NSV) problems of computing well-shaped geometric regions of an optimal total volume in a d-D weighted voxel grid. The NSV problems find applications in medical image segmentation and data mining. Our techniques yield the first polynomial time exact algorithms for several high dimensional medical image segmentation problems. Experiments and comparisons based on real medical data showed that our LNS algorithms and software are computationally efficient and produce highly accurate and consistent segmentation results.

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Ishii, Kenichiro. "Special Issue Image Processing. 2. Enriching the Technology of Image Processing. 2-4 Multi-Dimensional Image Processing." Journal of the Institute of Television Engineers of Japan 46, no. 11 (1992): 1418–28. http://dx.doi.org/10.3169/itej1978.46.1418.

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WU, ZHAOHUA, NORDEN E. HUANG, and XIANYAO CHEN. "THE MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION METHOD." Advances in Adaptive Data Analysis 01, no. 03 (July 2009): 339–72. http://dx.doi.org/10.1142/s1793536909000187.

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A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi-dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition (EEMD) to slices of data in each and every dimension involved. The final reconstruction of the corresponding intrinsic mode function (IMF) is based on a comparable minimal scale combination principle. For two-dimensional spatial data or images, f(x,y), we consider the data (or image) as a collection of one-dimensional series in both x-direction and y-direction. Each of the one-dimensional slices is decomposed through EEMD with the slice of the similar scale reconstructed in resulting two-dimensional pseudo-IMF-like components. This new two-dimensional data is further decomposed, but the data is considered as a collection of one-dimensional series in y-direction along locations in x-direction. In this way, we obtain a collection of two-dimensional components. These directly resulted components are further combined into a reduced set of final components based on a minimal-scale combination strategy. The approach for two-dimensional spatial data can be extended to multi-dimensional data. EEMD is applied in the first dimension, then in the second direction, and then in the third direction, etc., using the almost identical procedure as for the two-dimensional spatial data. A similar comparable minimal-scale combination strategy can be applied to combine all the directly resulted components into a small set of multi-dimensional final components. For multi-dimensional temporal-spatial data, EEMD is applied to time series of each spatial location to obtain IMF-like components of different time scales. All the ith IMF-like components of all the time series of all spatial locations are arranged to obtain ith temporal-spatial multi-dimensional IMF-like component. The same approach to the one used in temporal-spatial data decomposition is used to obtain the resulting two-dimensional IMF-like components. This approach could be extended to any higher dimensional temporal-spatial data.

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