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Journal articles on the topic 'Visual image reconstruction'

Author: Grafiati

Published: 21 September 2024

Last updated: 31 July 2025

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1

Nestor, Adrian, David C. Plaut, and Marlene Behrmann. "Feature-based face representations and image reconstruction from behavioral and neural data." Proceedings of the National Academy of Sciences 113, no. 2 (2015): 416–21. http://dx.doi.org/10.1073/pnas.1514551112.

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The reconstruction of images from neural data can provide a unique window into the content of human perceptual representations. Although recent efforts have established the viability of this enterprise using functional magnetic resonance imaging (MRI) patterns, these efforts have relied on a variety of prespecified image features. Here, we take on the twofold task of deriving features directly from empirical data and of using these features for facial image reconstruction. First, we use a method akin to reverse correlation to derive visual features from functional MRI patterns elicited by a la

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Bae, Joungeun, and Hoon Yoo. "Image Enhancement for Computational Integral Imaging Reconstruction via Four-Dimensional Image Structure." Sensors 20, no. 17 (2020): 4795. http://dx.doi.org/10.3390/s20174795.

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This paper describes the image enhancement of a computational integral imaging reconstruction method via reconstructing a four-dimensional (4-D) image structure. A computational reconstruction method for high-resolution three-dimensional (3-D) images is highly required in 3-D applications such as 3-D visualization and 3-D object recognition. To improve the visual quality of reconstructed images, we introduce an adjustable parameter to produce a group of 3-D images from a single elemental image array. The adjustable parameter controls overlapping in back projection with a transformation of crop

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Wang, Xia, and Qianqian Hu. "Visual Truth and Image Manipulation: Visual Ethical Anomie and Reconstruction of Digital Photography." SHS Web of Conferences 155 (2023): 03018. http://dx.doi.org/10.1051/shsconf/202315503018.

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Digital photography has impacted the visual truth in terms of image operation technology. The paper analyzes the performance of image manipulation in digital photography, and asserts that the operation image’s resolution of visual truth can be described by three aspects: the fact deviation, the reconstruction of the situation, the trust crisis, the filter survival, the second reprint, and the value change. Consequently, there are three visual ethical problems associated with the manipulation of images by technology, the siege of images on people, and distortions of social order caused by the m

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Meng, Lu, and Chuanhao Yang. "Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI." Bioengineering 10, no. 10 (2023): 1117. http://dx.doi.org/10.3390/bioengineering10101117.

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The reconstruction of visual stimuli from fMRI signals, which record brain activity, is a challenging task with crucial research value in the fields of neuroscience and machine learning. Previous studies tend to emphasize reconstructing pixel-level features (contours, colors, etc.) or semantic features (object category) of the stimulus image, but typically, these properties are not reconstructed together. In this context, we introduce a novel three-stage visual reconstruction approach called the Dual-guided Brain Diffusion Model (DBDM). Initially, we employ the Very Deep Variational Autoencode

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Yang, Qi, and Jong Hoon Yang. "Virtual Reconstruction of Visually Conveyed Images under Multimedia Intelligent Sensor Network Node Layout." Journal of Sensors 2022 (February 2, 2022): 1–12. http://dx.doi.org/10.1155/2022/8367387.

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In this paper, multimedia intelligent sensing technology is applied to the virtual reconstruction of images to construct or restore images to the communication media for visual communication. This paper proposes image virtual reconstruction theory based on visual communication research, treats image virtual reconstruction content as open data links and customized domain ontology, establishes an interdisciplinary interactive research framework through the technical means of visual communication, solves the problem of data heterogeneity brought by image virtual reconstruction, and finally establ

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Kumar, L. Ravi, K. G. S. Venkatesan, and S.Ravichandran. "Cloud-enabled Internet of Things Medical Image Processing Compressed Sensing Reconstruction." International Journal of Scientific Methods in Intelligence Engineering Networks 01, no. 04 (2023): 11–21. http://dx.doi.org/10.58599/ijsmien.2023.1402.

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Deep learning compresses medical image processing in IoMT. CS-MRI acquires quickly. It has various medicinal uses due to its advantages. This lowers motion artifacts and contrast washout. Reduces patient pressure and scanning costs. CS-MRI avoids the Nyquist-Shannon sampling barrier. Parallel imagingbased fast MRI uses many coils to reconstruct MRI images with less raw data. Parallel imaging enables rapid MRI. This research developed a deep learning-based method for reconstructing CS-MRI images that bridges the gap between typical non-learning algorithms that employ data from a single image an

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Yin, Jing, and Jong Hoon Yang. "Virtual Reconstruction Method of Regional 3D Image Based on Visual Transmission Effect." Complexity 2021 (June 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/5616826.

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In order to solve the problems of poor user experience and low human-computer interaction efficiency, this paper designs a 3D image virtual reconstruction system based on visual communication effects. First, the functional framework diagram and hardware structure diagram of the image 3D reconstruction system are given. Then, combined with the basic theory of visual communication design, the characteristics of different elements in the three-dimensional image system and reasonable visual communication forms are analyzed, and design principles are proposed to improve user experience and communic

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Sun, Chuilian, Chunmeng Wang, and Chen He. "Image Super-Resolution Reconstruction Algorithm Based on SRGAN and Swin Transformer." Symmetry 17, no. 3 (2025): 337. https://doi.org/10.3390/sym17030337.

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Existing methods have problems such as loss of details and insufficient reconstruction effect when processing complex images. To improve the quality and efficiency of image super-resolution reconstruction, this study proposes an improved algorithm based on super-resolution generative adversarial network and Swin Transformer. Firstly, on the ground of the traditional super-resolution generative adversarial network, combined with the global feature extraction capability of Swin Transformer, the model’s capacity to capture multi-scale features and restore details is enhanced. Subsequently, by uti

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Gong, Zixuan, Qi Zhang, Guangyin Bao, et al. "MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 13 (2025): 14247–55. https://doi.org/10.1609/aaai.v39i13.33560.

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Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks. Reconstructing high-quality images in cross-subject tasks is a challenging problem due to profound individual differences between subjects and the scarcity of data annotation. In this work, we proposed MindTuner for cross-subject visual decoding, which achieves high-quality and rich semantic reconstructions using only 1 hour of fMRI training data benefiting from the phenomena of visual fingerprint in the human visual system and a novel fM

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Njølstad, Tormund, Anselm Schulz, Johannes C. Godt, et al. "Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique – initial clinical experience." Acta Radiologica Open 10, no. 4 (2021): 205846012110083. http://dx.doi.org/10.1177/20584601211008391.

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Background A novel Deep Learning Image Reconstruction (DLIR) technique for computed tomography has recently received clinical approval. Purpose To assess image quality in abdominal computed tomography reconstructed with DLIR, and compare with standardly applied iterative reconstruction. Material and methods Ten abdominal computed tomography scans were reconstructed with iterative reconstruction and DLIR of medium and high strength, with 0.625 mm and 2.5 mm slice thickness. Image quality was assessed using eight visual grading criteria in a side-by-side comparative setting. All series were pres

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Xu, Li, Ling Bai, and Lei Li. "The Effect of 3D Image Virtual Reconstruction Based on Visual Communication." Wireless Communications and Mobile Computing 2022 (January 5, 2022): 1–8. http://dx.doi.org/10.1155/2022/6404493.

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Considering the problems of poor effect, long reconstruction time, large mean square error (MSE), low signal-to-noise ratio (SNR), and structural similarity index (SSIM) of traditional methods in three-dimensional (3D) image virtual reconstruction, the effect of 3D image virtual reconstruction based on visual communication is proposed. Using the distribution set of 3D image visual communication feature points, the feature point components of 3D image virtual reconstruction are obtained. By iterating the 3D image visual communication information, the features of 3D image virtual reconstruction

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Li, Yuting. "Design of 3D Image Visual Communication System for Automatic Reconstruction of Digital Images." Advances in Multimedia 2022 (July 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/3369386.

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In order to improve the visual communication effect of the 3D image visual communication system, this article combines the automatic reconstruction technology of digital images to design the 3D image visual communication system. Moreover, this article studies the shock stability of the shock capture scheme by combining entropy generation analysis, linear disturbance analysis, and numerical experiments. In addition to this, this article presents a general method that can be used to suppress numerical shock instabilities in various Godunov-type schemes. It can be seen from the experimental resul

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Xiao, Di, Yue Li, and Min Li. "Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing." Sensors 23, no. 7 (2023): 3575. http://dx.doi.org/10.3390/s23073575.

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Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which

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Wang, Jingjing, Fangyan Dong, Takashi Takegami, Eiroku Go, and Kaoru Hirota. "A 3D Pseudo-Reconstruction from Single ImageBased on Vanishing Point." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 4 (2009): 393–99. http://dx.doi.org/10.20965/jaciii.2009.p0393.

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A 3D (three-dimensional) pseudo-reconstruction method from a single image is presented as a novel approach reconstructing a 3D model with no prior internal knowledge of outdoors image. In the proposed method, an image is represented as a collection of sky layer, ground layer, and object layer. A visual radical coordinate system with vanishing point is established to accommodate the extracted 3D data from images. Learning method is done via the layers database. The experiment results show that the visually acceptable 3D model can be extracted less than one minute. That means a higher resolution

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Mao, Wenli, and Bingyu Zhang. "Research on the Application of Visual Sensing Technology in Art Education." Journal of Sensors 2021 (November 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/2406351.

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It is essential to have a new understanding of the development of visual sensing technology in digital image art at this stage, in order to make traditional art education have new professional ability teaching. Based on the current research results in related fields, a three-dimensional (3D) image visual communication system based on digital image automatic reconstruction is proposed with two schemes as the premise. In scheme 1, the hardware part is divided into two modules. The hardware used by the analysis of the 3D image layer module is the HUJ-23 3D image processor. The acquisition of a 3D

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Wang, Rongfang, Yali Qin, Zhenbiao Wang, and Huan Zheng. "Group-Based Sparse Representation for Compressed Sensing Image Reconstruction with Joint Regularization." Electronics 11, no. 2 (2022): 182. http://dx.doi.org/10.3390/electronics11020182.

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Achieving high-quality reconstructions of images is the focus of research in image compressed sensing. Group sparse representation improves the quality of reconstructed images by exploiting the non-local similarity of images; however, block-matching and dictionary learning in the image group construction process leads to a long reconstruction time and artifacts in the reconstructed images. To solve the above problems, a joint regularized image reconstruction model based on group sparse representation (GSR-JR) is proposed. A group sparse coefficients regularization term ensures the sparsity of

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Cagáň, Jan, Jaroslav Pelant, Martin Kyncl, Martin Kadlec, and Lenka Michalcová. "Damage detection in carbon fiber–reinforced polymer composite via electrical resistance tomography with Gaussian anisotropic regularization." Structural Health Monitoring 18, no. 5-6 (2018): 1698–710. http://dx.doi.org/10.1177/1475921718820013.

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Electrical resistance tomography is a method for sensing the spatial distribution of electrical conductivity. Therefore, this type of tomography is suitable for sensing damages, which affect electrical conductivity. The utilization of resistance tomography for the structural health monitoring of carbon fiber–reinforced polymer composites is questionable owing to its low spatial resolution and the strong anisotropy of carbon fiber–reinforced polymer composites. This article deals with the employment of resistance tomography with regularization based on a Gaussian anisotropic smoothing filter fo

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Cheng, Rongjie. "Computer Graphic Image Design and Visual Communication Design in the Internet of Things Scenario." Security and Communication Networks 2022 (May 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/8460433.

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For the problems of low-illumination reconstructed images with low resolution and long reconstruction time, an image reconstruction system based on the wavelet domain chunking compressed perception algorithm is proposed. A low-illumination image sampling model is established, and the wavelet domain chunking compression perception and information fusion processing are performed using the depth-of-field adaptive adjustment method of the image. The multiscale Retinex algorithm is used for wavelet domain chunking compressed perception and information extraction, and the information entropy feature

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Zhao, Yuqing, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang, and Min Yue. "Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images." International Journal of Digital Multimedia Broadcasting 2021 (July 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/5519508.

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The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution reconstruction of infrared images that preserves the edge structure and better visual quality is still challenging. Aiming at the problems of low resolution and unclear edges of infrared images, this work proposes a two-stage generative adversarial network model to reconstruct realistic superresolution images from four times downsampled infrared imag

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Liu, Ya-Li, Tao Liu, Bin Yan, Jeng-Shyang Pan, and Hong-Mei Yang. "Visual Cryptography Using Computation-Free Bit-Plane Reconstruction." Security and Communication Networks 2022 (June 26, 2022): 1–14. http://dx.doi.org/10.1155/2022/4617885.

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Visual cryptography (VC) using bit-plane decomposition improves the quality of the reconstructed image. The disadvantage of this scheme is that the decoder needs computation in order to reconstruct the secret image from its bit-planes. To solve this problem, we propose a no-computation bit-plane decomposition visual cryptography (NC-BPDVC). In NC-BPDVC, we convert the grayscale secret image into a multitone image by multilevel halftoning. Then, by exploring the difference between a digital pixel and a printed dot, we design different dot patterns to render a digital pixel. By doing so, we aban

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Yun, Bai. "Design and Reconstruction of Visual Art Based on Virtual Reality." Security and Communication Networks 2021 (August 19, 2021): 1–9. http://dx.doi.org/10.1155/2021/1014017.

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Because traditional methods generally lack the image preprocessing link, the effect of visual image detail processing is not good. In order to enhance the image visual effect, a visual art design method based on virtual reality is proposed. Wavelet transform method is used to denoise the visual image and the noise signal in the image is removed; a binary model of fuzzy space vision fusion is established, the space of the visual image is planned, and the spatial distribution information of the visual image is obtained. According to the principle of light and shadow phenomenon in visual image re

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Gillmann, Christina, Pablo Arbelaez, Jose Hernandez, Hans Hagen, and Thomas Wischgoll. "An Uncertainty-Aware Visual System for Image Pre-Processing." Journal of Imaging 4, no. 9 (2018): 109. http://dx.doi.org/10.3390/jimaging4090109.

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Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input

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Shao, Wenze, Haisong Deng, and Zhuihui Wei. "Nonconvex Compressed Sampling of Natural Images and Applications to Compressed MR Imaging." ISRN Computational Mathematics 2012 (November 16, 2012): 1–12. http://dx.doi.org/10.5402/2012/982792.

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There have been proposed several compressed imaging reconstruction algorithms for natural and MR images. In essence, however, most of them aim at the good reconstruction of edges in the images. In this paper, a nonconvex compressed sampling approach is proposed for structure-preserving image reconstruction, through imposing sparseness regularization on strong edges and also oscillating textures in images. The proposed approach can yield high-quality reconstruction as images are sampled at sampling ratios far below the Nyquist rate, due to the exploitation of a kind of approximate l0 seminorms.

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Buckley, Neil, Atulya Nagar, and Subramanian Arumugam. "On Real-valued Visual Cryptographic Basis Matrices." JUCS - Journal of Universal Computer Science 21, no. (12) (2015): 1536–62. https://doi.org/10.3217/jucs-021-12-1536.

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Visual cryptography (VC) encodes an image into noise-like shares, which can be stacked to reveal a reduced quality version of the original. The problem with encrypting colour images is that they must undergo heavy pre-processing to reduce them to binary, entailing significant quality loss. This paper proposes VC that works directly on intermediate grayscale values per colour channel and demonstrates real-valued basis matrices for this purpose. The resulting stacked shares produce a clearer reconstruction than in binary VC, and to the best of the authors' knowledge, is the first method posing n

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Chen, Huihong, and Shiming Li. "Simulation of 3D Image Reconstruction in Rigid body Motion." MATEC Web of Conferences 232 (2018): 02002. http://dx.doi.org/10.1051/matecconf/201823202002.

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3D image reconstruction under rigid body motion is affected by rigid body motion and visual displacement factors, which leads to low quality of 3D image reconstruction and more noise, in order to improve the quality of 3D image reconstruction of rigid body motion. A 3D image reconstruction technique is proposed based on corner detection and edge contour feature extraction in this paper. Region scanning and point scanning are combined to scan rigid body moving object image. The wavelet denoising method is used to reduce the noise of the 3D image. The edge contour feature of the image is extract

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Jiang, Lingyun, Kai Qiao, Linyuan Wang, et al. "Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare." Applied Sciences 9, no. 22 (2019): 4749. http://dx.doi.org/10.3390/app9224749.

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Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired b

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Li, Chun-mei, Ka-zhong Deng, Jiu-yun Sun, and Hui Wang. "Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction." Journal of Sensors 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/1250538.

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The spatial resolution of digital images is the critical factor that affects photogrammetry precision. Single-frame, superresolution, image reconstruction is a typical underdetermined, inverse problem. To solve this type of problem, a compressive, sensing, pseudodictionary-based, superresolution reconstruction method is proposed in this study. The proposed method achieves pseudodictionary learning with an available low-resolution image and uses theK-SVD algorithm, which is based on the sparse characteristics of the digital image. Then, the sparse representation coefficient of the low-resolutio

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Zeng, Yujie, Jin Lei, Tianming Feng, et al. "Neural Radiance Fields-Based 3D Reconstruction of Power Transmission Lines Using Progressive Motion Sequence Images." Sensors 23, no. 23 (2023): 9537. http://dx.doi.org/10.3390/s23239537.

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To address the fuzzy reconstruction effect on distant objects in unbounded scenes and the difficulty in feature matching caused by the thin structure of power lines in images, this paper proposes a novel image-based method for the reconstruction of power transmission lines (PTLs). The dataset used in this paper comprises PTL progressive motion sequence datasets, constructed by a visual acquisition system carried by a developed Flying–walking Power Line Inspection Robot (FPLIR). This system captures close-distance and continuous images of power lines. The study introduces PL-NeRF, that is, an e

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Basha, Shaik Mahaboob, and B. C. Jinaga. "A Novel Optimized Golomb-Rice Technique for the Reconstruction in Lossless Compression of Digital Images." ISRN Signal Processing 2013 (August 7, 2013): 1–5. http://dx.doi.org/10.1155/2013/539759.

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The research trends that are available in the area of image compression for various imaging applications are not adequate for some of the applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm based on G

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Vacher, Jonathan, Claire Launay, Pascal Mamassian, and Ruben Coen-Cagli. "Measuring uncertainty in human visual segmentation." PLOS Computational Biology 19, no. 9 (2023): e1011483. http://dx.doi.org/10.1371/journal.pcbi.1011483.

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Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple pixel-based same–different judgments and p

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Sonavane, Mr Mayur M., and Dr S. S. Agrawal. "Comparison of different algorithms to improve the quality of Image." IOSR Journal of VLSI and Signal processing 14, no. 4 (2024): 11–17. http://dx.doi.org/10.9790/4200-14041117.

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Super-resolution (SR) become the most important task in image processing because of requirement of high quality of images. It becomes popular in technology like computer vision and pattern recognition. In Single Image Super-resolution (SR) uses the sequences of single low resolution (LR) images from high resolution (HR) to improve the quality of image. The SR is an image processing problem which aims to generate a high resolution (HR) image from low resolution input image. The SR is difficult because of the missing information in the given LR image. Thus in SR it is very important to boost the

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Li, Jiefei, Yuqi Zhang, Le He, and Huancong Zuo. "Application of Multimodal Image Fusion Technology in Brain Tumor Surgical Procedure." Translational Neuroscience and Clinics 2, no. 4 (2016): 215–26. http://dx.doi.org/10.18679/cn11-6030_r.2016.035.

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Objective To construct brain tumors and their surrounding anatomical structures through the method of registration, fusion and, three-dimensional (3D) reconstruction based on multimodal image data and to provide the visual information of tumor, skull, brain, and vessels for preoperative evaluation, surgical planning, and function protection. Methods The image data of computed tomography (CT) and magnetic resonance imaging (MRI) were collected from fifteen patients with confirmed brain tumors. We reconstructed brain tumors and their surrounding anatomical structures using NeuroTech software. Re

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Yang, Qiang, and Huajun Wang. "Super-resolution reconstruction for a single image based on self-similarity and compressed sensing." Journal of Algorithms & Computational Technology 12, no. 3 (2018): 234–44. http://dx.doi.org/10.1177/1748301818778244.

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Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse representation and reconstruction algorithm in compressed sensing theory, super-resolution reconstruction (SRSR) of a single image was realized. The proposed algorithm adopted improved K-SVD algorithm for sample training and learning dictionary construction; additionally, the matching pursuit algorithm was improved for achieving single-image SRS

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Hanry, Ham, Wesley Julian, and Hendra. "Computer vision based 3D reconstruction : A review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2394–402. https://doi.org/10.11591/ijece.v9i4.pp2394-2402.

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3D reconstruction are used in many fields starts from the object reconstruction such as site, cultural artifacts in both ground and under the sea levels, medical imaging data, nuclear substantional. The scientist are beneficial for these task in order to learn, keep and better visual enhancement into 3D data. In this paper we differentiate the algorithm used depends on the input image: single still image, RGB-Depth image, multiperspective of 2D images, and video sequences. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.

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Xiang, Wenzhao, Chang Liu, Hongyang Yu, and Xilin Chen. "Wavelet-Driven Masked Image Modeling: A Path to Efficient Visual Representation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8611–19. https://doi.org/10.1609/aaai.v39i8.32930.

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Masked Image Modeling (MIM) has garnered significant attention in self-supervised learning, thanks to its impressive capacity to learn scalable visual representations tailored for downstream tasks. However, images inherently contain abundant redundant information, leading the pixel-based MIM reconstruction process to focus excessively on finer details such as textures, thus prolonging training times unnecessarily. Addressing this challenge requires a shift towards a compact representation of features during MIM reconstruction. Frequency domain analysis provides a promising avenue for achieving

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Condorelli, Francesca, and Maurizio Perticarini. "Comparative Evaluation of NeRF Algorithms on Single Image Dataset for 3D Reconstruction." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 73–79. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-73-2024.

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Abstract. The reconstruction of three-dimensional scenes from a single image represents a significant challenge in computer vision, particularly in the context of cultural heritage digitisation, where datasets may be limited or of poor quality. This paper addresses this challenge by conducting a study of the latest and most advanced algorithms for single-image 3D reconstruction, with a focus on applications in cultural heritage conservation. Exploiting different single-image datasets, the research evaluates the strengths and limitations of various artificial intelligence-based algorithms, in p

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Boscain, Ugo, Roman Chertovskih, Jean-Paul Gauthier, Dario Prandi, and Alexey Remizov. "Cortical-inspired image reconstruction via sub-Riemannian geometry and hypoelliptic diffusion." ESAIM: Proceedings and Surveys 64 (2018): 37–53. http://dx.doi.org/10.1051/proc/201864037.

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In this paper we review several algorithms for image inpainting based on the hypoelliptic diffusion naturally associated with a mathematical model of the primary visual cortex. In particular, we present one algorithm that does not exploit the information of where the image is corrupted, and others that do it. While the first algorithm is able to reconstruct only images that our visual system is still capable of recognize, we show that those of the second type completely transcend such limitation providing reconstructions at the state-of-the-art in image inpainting. This can be interpreted as a

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Liu, Chengwen, Bin Liao, and Zhuoyue Chang. "BMFusion: Bridging the Gap Between Dark and Bright in Infrared-Visible Imaging Fusion." Electronics 13, no. 24 (2024): 5005. https://doi.org/10.3390/electronics13245005.

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The fusion of infrared and visible light images is a crucial technology for enhancing visual perception in complex environments. It plays a pivotal role in improving visual perception and subsequent performance in advanced visual tasks. However, due to the significant degradation of visible light image quality in low-light or nighttime scenes, most existing fusion methods often struggle to obtain sufficient texture details and salient features when processing such scenes. This can lead to a decrease in fusion quality. To address this issue, this article proposes a new image fusion method calle

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Maitin, Ana M., Alberto Nogales, Emilio Delgado-Martos, et al. "Evaluating Activation Functions in GAN Models for Virtual Inpainting: A Path to Architectural Heritage Restoration." Applied Sciences 14, no. 16 (2024): 6854. http://dx.doi.org/10.3390/app14166854.

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Computer vision has advanced much in recent years. Several tasks, such as image recognition, classification, or image restoration, are regularly solved with applications using artificial intelligence techniques. Image restoration comprises different use cases such as style transferring, improvement of quality resolution, or completing missing parts. The latter is also known as image inpainting, virtual image inpainting in this case, which consists of reconstructing missing regions or elements. This paper explores how to evaluate the performance of a deep learning method to do virtual image inp

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Shan, Feng, and Youya Wang. "Animation Design Based on 3D Visual Communication Technology." Scientific Programming 2022 (January 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/6461538.

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The depth synthesis of image texture is neglected in the current image visual communication technology, which leads to the poor visual effect. Therefore, the design method of film and TV animation based on 3D visual communication technology is proposed. Collect film and television animation videos through 3D visual communication content production, server processing, and client processing. Through stitching, projection mapping, and animation video image frame texture synthesis, 3D vision conveys animation video image projection. In order to ensure the continuous variation of scaling factors be

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Khaleghi, Nastaran, Tohid Yousefi Rezaii, Soosan Beheshti, Saeed Meshgini, Sobhan Sheykhivand, and Sebelan Danishvar. "Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network." Electronics 11, no. 21 (2022): 3637. http://dx.doi.org/10.3390/electronics11213637.

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Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown that brain activity is impressed by visual saliency, the important parts of an image stimuli. In this paper, a deep model is proposed to reconstruct the image stimuli from electroencephalogram (EEG) recordings via visual saliency. To this end, the pro

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Xiao, Yahui. "Research on Visual Image Texture Rendering for Artistic Aided Design." Scientific Programming 2021 (August 7, 2021): 1–8. http://dx.doi.org/10.1155/2021/1190912.

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The rendering effect of known visual image texture is poor and the output image is not always clear. To solve this problem, this paper proposes a visual image rendering based on scene visual understanding algorithm. In this approach, the color segmentation of known visual scene is carried out according to a predefined threshold, and the segmented image is processed by morphology. For this purpose, the extraction rules are formulated to screen the candidate regions. The color image is fused and filtered in the neighborhood, the pixels of the image are extracted, and the 2D texture recognition i

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Takeshi, Nii, Hosokawa Shota, Kotani Tomoya, Nakamura Yasunori, Kondo Ryotaro, and Takahashi Yasuyuki. "Image reconstruction parameters and the standardized uptake value ratios in brain amyloid PET." Nuclear Medicine Communications 45, no. 11 (2024): 984–91. http://dx.doi.org/10.1097/mnm.0000000000001899.

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Objectives The present study investigated various image reconstruction protocols for amyloid PET using phantom test criteria published by the Japanese Society of Nuclear Medicine (JSNM) and compared them with the composite standardized uptake value ratio (cSUVR) in clinical imaging. Methods Hoffman 3D phantoms and cylindrical phantoms were collected for 30 min according to the JSNM guidelines. Images were created under various reconstruction protocols by three physical evaluation items in the guidelines and were assessed: gray matter/white matter contrast (%contrast), uniformity (SDuROImean),

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Lim, Seung-Chan, and Myungjin Cho. "Three-Dimensional Image Transmission of Integral Imaging through Wireless MIMO Channel." Sensors 23, no. 13 (2023): 6154. http://dx.doi.org/10.3390/s23136154.

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For the reconstruction of high-resolution 3D digital content in integral imaging, an efficient wireless 3D image transmission system is required to convey a large number of elemental images without a communication bottleneck. To support a high transmission rate, we herein propose a novel wireless three-dimensional (3D) image transmission and reception strategy based on the multiple-input multiple-output (MIMO) technique. By exploiting the spatial multiplexing capability, multiple elemental images are transmitted simultaneously through the wireless MIMO channel, and recovered with a linear rece

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Wang, Yuhang. "Image 3D Reconstruction and Interaction Based on Digital Twin and Visual Communication Effect." Mobile Information Systems 2022 (July 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/8510369.

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With the continuous development of multimedia technology and electronic information technology, the application of 3D images has become more and more extensive, and 3D reconstruction and interactive technology have also received more and more attention from researchers. Digital twin (DT for short) is a technology that makes full use of digital twin models and real-time monitoring data to complete real-time mapping of the real world in the digital world. Visual communication (CV for short) design is a design that is expressed and communicated to the audience through a visual medium. 3D reconstr

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Liya, Abel, Resti Ningsih, Rafi Hidayat, and Taufik Ramadhan Firdaus. "Medical Image Reconstruction in MRI Using Interpolation." International Journal of Technology and Modeling 3, no. 1 (2024): 24–34. https://doi.org/10.63876/ijtm.v3i1.99.

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Medical image reconstruction is a crucial element in magnetic resonance imaging (MRI) to produce high-quality images that support clinical diagnosis. This study aims to develop a medical image reconstruction method based on interpolation techniques that improves spatial accuracy and visual detail in MRI imaging results. The methodology used includes the implementation of bilinear and bicubic interpolation algorithms to process signal data obtained from MRI imaging. The dataset used in this study is brain MRI data from an open database that has been validated. The results show that the bilinear

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Fazel-Rezai, Reza, and Witold Kinsner. "Modified Gabor Wavelets for Image Decomposition and Perfect Reconstruction." International Journal of Cognitive Informatics and Natural Intelligence 3, no. 4 (2009): 19–33. http://dx.doi.org/10.4018/jcini.2009062302.

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This article presents a scheme for image decomposition and perfect reconstruction based on Gabor wavelets. Gabor functions have been used extensively in areas related to the human visual system due to their localization in space and bandlimited properties. However, since the standard two-sided Gabor functions are not orthogonal and lead to nearly singular Gabor matrices, they have been used in the decomposition, feature extraction, and tracking of images rather than in image reconstruction. In an attempt to reduce the singularity of the Gabor matrix and produce reliable image reconstruction, i

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Lu, Qiuju. "Local Defogging Algorithm for Improving Visual Impact in Image Based on Multiobjective Optimization." Mathematical Problems in Engineering 2022 (July 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/7200657.

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The preprocessing of images is required for many applications based on industry, social, and academic requirements. Researchers have developed a number of techniques to improve the visual effect of images and appropriately interpret visual effects. The accuracy of visuals is important in cyber security, military organization, police organizations, and forensics to detect the true story from the pictures. They search for evidence by digging deep into the network in search of evidence. If visuals are not clear, preprocessing of images is not done correctly, then it may lead to wrong interpretati

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Zhang, Xiaoning, Zhaoyang Peng, Yifei Wang, Fan Ye, Tengying Fu, and Hu Zhang. "A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks." Remote Sensing 17, no. 11 (2025): 1901. https://doi.org/10.3390/rs17111901.

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Multispectral images contain richer spectral signatures than easily available RGB images, for which they are promising to contribute to information perception. However, the relatively high cost of multispectral sensors and lower spatial resolution limit the widespread application of multispectral data, and existing reconstruction algorithms suffer from a lack of diverse training datasets and insufficient reconstruction accuracy. In response to these issues, this paper proposes a novel and robust multispectral reconstruction network from low-cost natural color RGB images based on free available

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Huo, Jiaofei, and Xiaomo Yu. "Three-dimensional mechanical parts reconstruction technology based on two-dimensional image." International Journal of Advanced Robotic Systems 17, no. 2 (2020): 172988142091000. http://dx.doi.org/10.1177/1729881420910008.

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With the development of computer technology and three-dimensional reconstruction technology, three-dimensional reconstruction based on visual images has become one of the research hotspots in computer graphics. Three-dimensional reconstruction based on visual image can be divided into three-dimensional reconstruction based on single photo and video. As an indirect three-dimensional modeling technology, this method is widely used in the fields of film and television production, cultural relics restoration, mechanical manufacturing, and medical health. This article studies and designs a stereo v

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