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Artículos de revistas sobre el tema "Reconstruction de pixel"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Rymarczyk, Tomasz, and Grzegorz Kłosowski. "Identification of moisture inside walls in buildings using machine learning and ensemble methods." International Journal of Applied Electromagnetics and Mechanics 69, no. 3 (2022): 375–88. http://dx.doi.org/10.3233/jae-210176.

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According to the article, locating moisture within the walls of buildings using electrical impedance tomography is discussed in detail. The algorithmic approach, whose role is to convert the input measurements into images, received excellent attention during the development process. Numerous models have been trained to generate tomographic images based on individual pixels in a given image based on machine learning methods. An array of categorisation data was then generated, which enabled the development of a classification model to solve the problem of optimal model selection for a given poin

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2

Mantripragada, Kiran, Phuong D. Dao, Yuhong He, and Faisal Z. Qureshi. "The effects of spectral dimensionality reduction on hyperspectral pixel classification: A case study." PLOS ONE 17, no. 7 (2022): e0269174. http://dx.doi.org/10.1371/journal.pone.0269174.

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This paper presents a systematic study of the effects of hyperspectral pixel dimensionality reduction on the pixel classification task. We use five dimensionality reduction methods—PCA, KPCA, ICA, AE, and DAE—to compress 301-dimensional hyperspectral pixels. Compressed pixels are subsequently used to perform pixel classifications. Pixel classification accuracies together with compression method, compression rates, and reconstruction errors provide a new lens to study the suitability of a compression method for the task of pixel classification. We use three high-resolution hyperspectral image d

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3

Yuri, Hercilia Mejía-Melgarejo, Patricia Villarreal-Dulcey Ofelia, and Arguello-Fuentes Henry. "Adjustable spatial resolution of compressive spectral images sensed by multispectral filter array-based sensors." Revista Facultad de Ingeniería –redin-, no. 78 (March 19, 2016): 89–98. https://doi.org/10.17533/udea.redin.n78a12.

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Spectral imaging systems capture spectral and spatial information from a scene to produce a spectral data cube. Technical progress has allowed developing multispectral filter array (MSFA)-based sensors in order to expand the reconstruction of more bands than RGB cameras. However, reconstructing the spectral image with traditional methods following a least squares or demosaicing approach is unfeasible. Some works in the literature implement multispectral demosaicing for reconstructing images with specific spatio-spectral resolution depending on the number of pixels in the detector and the filte

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4

Hirata, Christopher M., and Christopher Merchant. "Pixel Centroid Characterization with Laser Speckle and Application to the Nancy Grace Roman Space Telescope Detector Arrays." Publications of the Astronomical Society of the Pacific 134, no. 1041 (2022): 115001. http://dx.doi.org/10.1088/1538-3873/ac99fe.

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Abstract The Nancy Grace Roman Space Telescope will use its wide-field instrument to carry out a suite of sky surveys in the near-infrared. Several of the science objectives of these surveys, such as the measurement of the growth of cosmic structure using weak gravitational lensing, require exquisite control of instrument-related distortions of the images of astronomical objects. Roman will fly new large-format (4 × 4 k) Teledyne H4RG-10 infrared detector arrays. This paper investigates whether the pixel centroids are located on a regular grid by projecting laser speckle patterns through a dou

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5

Ahmed, Ansari Vaqar, and Uday Pandit Khot. "An Efficient Motion Vector Recovery and Reconstruction Method for Spatiotemporal Video Error Concealment." International Journal of Computer Vision and Image Processing 9, no. 4 (2019): 28–48. http://dx.doi.org/10.4018/ijcvip.2019100103.

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In this article, an efficient spatiotemporal video error concealment (EC) based on motion vector (MV) recovery and a pixel reconstruction (PR) method is proposed. The pixel-based motion vector with partition (PMVP) is modified by using Mahalanobis distance (MD) rather than Euclidean distance (ED) for recovering MVs, as MD uses standard deviation and covariance of available pixels. Further, the MD gives more accuracy for non-square cluster compared to ED. This modified pixel-based motion vector with partition (MPMVP) algorithm is further upgrade by two different strategies. First, by using voti

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6

Alanazi, Turki M., and Paolo Mercorelli. "Precision Denoising in Medical Imaging via Generative Adversarial Network-Aided Low-Noise Discriminator Technique." Mathematics 12, no. 23 (2024): 3705. http://dx.doi.org/10.3390/math12233705.

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Medical imaging is significant for accurate diagnosis, and here, noise often degrades image quality, thus making it challenging to identify important information. Denoising is a component of traditional image pre-processing that helps prevent incorrect disease diagnosis. Mitigating the noise becomes difficult if there are differences in the low-level segment features. Therefore, a Generative Adversarial Network (GAN)-aided Low-Noise Discriminator (LND) is introduced to improve the denoising effectiveness in medical images with a balanced image resolution with noise mitigation. The LND function

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7

Irum, Isma, Muhammad Sharif, Mussarat Yasmin, Mudassar Raza, and Faisal Azam. "A Noise Adaptive Approach to Impulse Noise Detection and Reduction." Nepal Journal of Science and Technology 15, no. 1 (2015): 67–76. http://dx.doi.org/10.3126/njst.v15i1.12016.

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A noise adaptive filter has been proposed in this study aiming to estimate the original image pixel values in the presence of impulse noise in monochromatic images. The proposed filter approach is noise adaptive that as the percentage of noise density increases in the image, the size of neighborhood in filtering window is also increased. Proposed approach comprises of two stages, one is impulse noise detection and the other is impulse noise reduction or cancellation. First stage is based on median and mean distance and thresholding whereas the second stage is based on reconstruction of the ima

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8

Tian, Y., W. Zhou, Q. Wang, et al. "A novel silicon pixel sensor for beam monitoring applications at heavy-ion accelerators." Journal of Instrumentation 19, no. 04 (2024): C04039. http://dx.doi.org/10.1088/1748-0221/19/04/c04039.

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Abstract This paper describes a silicon pixel sensor for non-interceptive real-time beam monitoring at heavy-ion accelerators. The total size of the sensor is 4 mm × 5 mm. It has 64 (row) × 120 (column) square pixels, each single of which is in the size of 40 μm × 40 μm. With the exposed sensing pad, this sensor can directly collect the charge in the media over the pixels. The in-pixel circuit mainly consists of a low-noise Charge Sensitive Amplifier (CSA) to establish the signal for the energy reconstruction and a discriminator with a Time-to-Amplitude Converter (TAC) for the Time of Arrival

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9

Šperl, Ondřej, and Jan Sýkora. "Reconstruction of concrete morphology using deep learning." Acta Polytechnica CTU Proceedings 49 (November 21, 2024): 85–91. https://doi.org/10.14311/app.2024.49.0085.

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In this contribution, the concrete morphology is reconstructed with a simple algorithm selecting a pixel value based on the small set of surrounding pixels. A deep neural network (DNN) is used as a classifier, and the authors focus on studying different DNN architectures. The performance of the proposed algorithm is evaluated on several statistical descriptors and the grain size distributioncurve.

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10

Diógenes, A. N., L. O. E. Dos Santos, C. P. Fernandes, A. C. Moreira, and C. R. Apolloni. "POROUS MEDIA MICROSTRUCTURE RECONSTRUCTION USING PIXEL-BASED AND OBJECT-BASED SIMULATED ANNEALING – COMPARISON WITH OTHER RECONSTRUCTION METHODS." Revista de Engenharia Térmica 8, no. 2 (2009): 35. http://dx.doi.org/10.5380/reterm.v8i2.61896.

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In this contribution the issue of the stochastic reconstruction of particulatemedia from 2D photomicrographic images is addressed with particular reference to pore space connectivity. The reconstruction of porous bodies in 2D or 3D space was achieved by using simulated annealing techniques. Two methods were proposed to reconstruct a well connected pore space. The first, named PSA (Pixel-based Simulated Annealing), a pixel-movement based, three constraints were found to be necessary for the successful reconstruction of well connected pore space: the two-pointcorrelation function, the d3-4 dista

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11

Li, L., L. Zhang, J. N. Dong, J. Liu, and M. Wang. "Characterization of a CMOS pixel sensor for charged particle tracking." Journal of Instrumentation 16, no. 12 (2021): P12016. http://dx.doi.org/10.1088/1748-0221/16/12/p12016.

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Abstract A prototype of the CMOS pixel sensor named Supix-1 has been fabricated and tested in order to investigate the feasibility of a pixelated tracker for a proposed Higgs factory, namely, the Circular Electron-Positron Collider (CEPC). The sensor, taped out with a 180 nm CMOS Image Sensor (CIS) process, consists of nine different pixel arrays varying in pixel pitches, diode sizes and geometries in order to study the particle detection performance of enlarged pixels. The test was carried out with a 55Fe radioactive source. Two soft X-ray peaks observed were used to calibrate the charge to v

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12

Fager, R. S., K. V. Peddanarappagari, and G. N. Kumar. "Pixel-based reconstruction (PBR) promising simultaneous techniques for CT reconstructions." IEEE Transactions on Medical Imaging 12, no. 1 (1993): 4–9. http://dx.doi.org/10.1109/42.222660.

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13

Pastuszak, Grzegorz. "Subsampling of 3D Pixel Blocks as a Video Compression Method for Analog Transmission." Electronics 12, no. 12 (2023): 2641. http://dx.doi.org/10.3390/electronics12122641.

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Hybrid digital–analog transmission of video signals enables flexibility in dividing video information into two parts to utilize the available bandwidth better. This study proposes a compression scheme to reduce the utilized bandwidth. The scheme uses different subsampling in three-dimensional (3D) blocks, where subsampling factors are selected to minimize reconstruction distortion. The study evaluates various methods for subsampling and reconstruction to find the best combination in terms of reconstruction quality and complexity. Results show that medium-quality reconstructions can be obtained

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14

Ahmed, Ansari Vaqar, and Uday Pandit Khot. "An Efficient Generalized Error Concealment in Video Codec." International Journal of Computer Vision and Image Processing 10, no. 4 (2020): 1–28. http://dx.doi.org/10.4018/ijcvip.2020100101.

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Efficient error concealment (EC) predictor can recover more significant features or structures of entire lost MBs using a pre-transmission algorithm (PTA) with convolutional neural network (CNN) and fuzzy reasoning to select appropriate EC for reconstruction in generalized video-codec compression scheme such as H.264/H.265, etc. Here, the pixel-based motion vector with partition (PMVP) algorithm is modified by using Mahalanobis distance (MD) rather than Euclidean distance (ED) for better MVs recovery. This modified pixel-based motion vector with partition (MPMVP) algorithm is upgraded by two d

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15

Wu, Wei, Luoqi Ge, Jiancheng Luo, Ruohong Huan, and Yingpin Yang. "A Spectral–Temporal Patch-Based Missing Area Reconstruction for Time-Series Images." Remote Sensing 10, no. 10 (2018): 1560. http://dx.doi.org/10.3390/rs10101560.

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Clouds, cloud shadows (CCS), and numerous other factors will cause a missing data problem in passive remote sensing images. A well-known reconstruction method is the selection of a similar pixel (with an additional clear reference image) from the remaining clear part of an image to replace the missing pixel. Due to the merit of filling the missing value using a pixel acquired on the same image with the same sensor and the same date, this method is suitable for time-series applications when a time-series profile-based similar measure is utilized for selecting the similar pixel. Since the simila

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16

Braach, Justus, Eric Buschmann, Dominik Dannheim, et al. "Performance of the FASTPIX Sub-Nanosecond CMOS Pixel Sensor Demonstrator." Instruments 6, no. 1 (2022): 13. http://dx.doi.org/10.3390/instruments6010013.

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Within the ATTRACT FASTPIX project, a monolithic pixel sensor demonstrator chip has been developed in a modified 180 nm CMOS imaging process, targeting sub-nanosecond timing measurements for single ionizing particles. It features a small collection electrode design on a 25 micron thick epitaxial layer and contains 32 mini matrices of 68 hexagonal pixels each, with pixel pitches ranging from 8.66 to 20 micron. Four pixels are transmitting an analog output signal and 64 are transmitting binary hit information. Various design variations are explored, aiming at accelerating the charge collection a

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17

Liang, Shi Guo, Ou Yang Yi, and Hui Wang. "Fast Multi-Layer 3D Reconstruction Algorithm." Advanced Materials Research 267 (June 2011): 827–30. http://dx.doi.org/10.4028/www.scientific.net/amr.267.827.

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A fast multi-layer 3d reconstruction algorithm is proposed to realize 3d body surface layer. Based on multi-layer surface reconstructing the shortest triangle line algorithm are proposed. In order to achieve more rapid reconstruction, a fast multi-layer 3D reconstruction algorithm is proposed. When the points on the layers having some errors, or have some offset, during the reconstruction of layered, it will automatically adjust for each pixel value of the selected layer, so that reconstruction process can be completed faster and more accurately . We give optimization method to realize fast re

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18

Liu, Fei, Xiaoming Zhu, Pingfa Feng, and Long Zeng. "Anomaly Detection via Progressive Reconstruction and Hierarchical Feature Fusion." Sensors 23, no. 21 (2023): 8750. http://dx.doi.org/10.3390/s23218750.

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The main challenges in reconstruction-based anomaly detection include the breakdown of the generalization gap due to improved fitting capabilities and the overfitting problem arising from simulated defects. To overcome this, we propose a new method called PRFF-AD, which utilizes progressive reconstruction and hierarchical feature fusion. It consists of a reconstructive sub-network and a discriminative sub-network. The former achieves anomaly-free reconstruction while maintaining nominal patterns, and the latter locates defects based on pre- and post-reconstruction information. Given defective

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19

Gustafson, Steven C., Gordon R. Little, John S. Loomis, and Todd S. Puterbaugh. "Optimal reconstruction of missing-pixel images." Applied Optics 31, no. 32 (1992): 6829. http://dx.doi.org/10.1364/ao.31.006829.

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20

Wei, Ziran, Jianlin Zhang, Wei Du, and Zhiruo Wang. "Real-time single-pixel video imaging based on deep learning." International Journal of Emerging Technologies and Advanced Applications 1, no. 12 (2025): 1–5. https://doi.org/10.62677/ijetaa.2412130.

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The emergence of compressed sensing (CS) theory has enabled the development of single-pixel cameras that achieve high-resolution imaging using a single photodetector. However, traditional CS reconstruction algorithms require significant computational time and face an inherent trade-off between imaging resolution and frame rate, limiting current single-pixel cameras to static scene imaging. A key challenge lies in achieving real-time single-pixel imaging with both high frame rate and high resolution. This paper proposes a real-time single-pixel imaging technology based on deep learning. We desi

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21

Zhao, Wenjing, Lei Gao, Aiping Zhai, and Dong Wang. "Comparison of Common Algorithms for Single-Pixel Imaging via Compressed Sensing." Sensors 23, no. 10 (2023): 4678. http://dx.doi.org/10.3390/s23104678.

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Single-pixel imaging (SPI) uses a single-pixel detector instead of a detector array with a lot of pixels in traditional imaging techniques to realize two-dimensional or even multi-dimensional imaging. For SPI using compressed sensing, the target to be imaged is illuminated by a series of patterns with spatial resolution, and then the reflected or transmitted intensity is compressively sampled by the single-pixel detector to reconstruct the target image while breaking the limitation of the Nyquist sampling theorem. Recently, in the area of signal processing using compressed sensing, many measur

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22

Kim, Youngjun, Jiyong Park, Jungsik Koo, Min-Chul Lee, and Myungjin Cho. "Optimum Pitch of Volumetric Computational Reconstruction in Integral Imaging." Electronics 13, no. 23 (2024): 4595. http://dx.doi.org/10.3390/electronics13234595.

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In this paper, we propose a method for how to find the optimum pitch of volumetric computational reconstruction (VCR) in integral imaging. In conventional VCR, the pixel shifts between elemental images are quantized due to pixel-based processing. As a result, quantization errors may occur during three-dimensional (3D) reconstruction in integral imaging. This may cause the degradation of the visual quality and depth resolution of the reconstructed 3D image. To overcome this problem, we propose a method to find the optimum pitch for VCR in integral imaging. To minimize the quantization error in

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23

Gao, Han, Guifeng Zhang, Min Huang, et al. "Three-Dimensional Pulsed-Laser Imaging via Compressed Sensing Reconstruction Based on Proximal Momentum-Gradient Descent." Remote Sensing 16, no. 23 (2024): 4601. https://doi.org/10.3390/rs16234601.

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Compressed sensing (CS) is a promising approach to enhancing the spatial resolution of images obtained from few-pixel array sensors in three-dimensional (3D) laser imaging scenarios. However, traditional CS-based methods suffer from insufficient range resolutions and poor reconstruction quality at low CS sampling ratios. To solve the CS reconstruction problem under the time-of-flight (TOF)-based pulsed-laser imaging framework, a CS algorithm based on proximal momentum-gradient descent (PMGD) is proposed in this paper. To improve the accuracy of the range and intensity reconstructed from overla

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24

Xu, Mingzhu, Rong Shang, Jing M. Chen, and Lingfang Zeng. "LACC2.0: Improving the LACC Algorithm for Reconstructing Satellite-Derived Time Series of Vegetation Biochemical Parameters." Remote Sensing 15, no. 13 (2023): 3277. http://dx.doi.org/10.3390/rs15133277.

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The locally adjusted cubic-spline capping (LACC) algorithm is well recognized for its effectiveness in the global time series reconstruction of vegetation biophysical and biochemical parameters. However, in its application, we often encounter issues, such as identifying positively biased outliers for vegetation biochemical parameters and reducing the influence of long consecutive gaps. In this study, we improved the LACC algorithm to address the above two issues by (1) incorporating a procedure to remove outliers and (2) integrating the spatial information of neighboring pixels for large data

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25

Carvalho, Francelino Freitas, Carlos Augusto de Moraes Cruz, Greicy C. Marques, and Thiago Brito Bezerra. "A Novel Hybrid CMOS Pixel-Cluster for Local Light Angle, Polarization and Intensity Detection with Determination of Stokes Parameters." Journal of Integrated Circuits and Systems 13, no. 2 (2018): 1–10. http://dx.doi.org/10.29292/jics.v13i2.7.

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Detecting local light incident angle is a desirable feature for CMOS image sensors for 3D image reconstruction purposes and depth sensing. Advances in the CMOS technologies in the last years have enabled integrated solutions to perform such a job. However, it is still not viable to implement such a feature in regular CMOS image sensors due to the great number of pixels in a cluster to perform incident angle detection. In this paper, a hybrid cluster with only four pixels, instead of eight pixels of previous solutions, that is able to detect both local light intensity, incident angle and Stokes

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26

Yu, Zihan, Cheng Zhang, Derek Nowrouzezahrai, Zhao Dong, and Shuang Zhao. "Efficient Differentiation of Pixel Reconstruction Filters for Path-Space Differentiable Rendering." ACM Transactions on Graphics 41, no. 6 (2022): 1–16. http://dx.doi.org/10.1145/3550454.3555500.

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Pixel reconstruction filters play an important role in physics-based rendering and have been thoroughly studied. In physics-based differentiable rendering, however, the proper treatment of pixel filters remains largely under-explored. We present a new technique to efficiently differentiate pixel reconstruction filters based on the path-space formulation. Specifically, we formulate the pixel boundary integral that models discontinuities in pixel filters and introduce new antithetic sampling methods that support differentiable path sampling methods, such as adjoint particle tracing and bidirecti

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27

Henao-Londoño, J. C., J. C. Riaño-Rojas, J. B. Gómez-Mendoza, and E. Restrepo-Parra. "3D Stereo Reconstruction of SEM Images." Modern Applied Science 12, no. 12 (2018): 57. http://dx.doi.org/10.5539/mas.v12n12p57.

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In this work is proposed a new fully automated methodology using computer vision and dynamic programming to obtain a 3D reconstruction model of surfaces using scanning electron microscope (SEM) images based on stereovision. The horizontal stereo matching step is done with a robust and efficient algorithm based on semi-global matching. The cost function used in this study is very simple since the brightness and contrast change of corresponding pixels is negligible for the small tilt involved in stereo SEM. It is used a sum of absolute differences (SAD) over a variable pixel size window. Since i

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28

Liu, Feng Lin, Quan Kang, and Bing He. "Permissible Deviation of Rotation Center Based on Fan-Beam Projection ICT System." Advanced Materials Research 452-453 (January 2012): 21–30. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.21.

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For ICT wide fan-beam scanning, there is a geometrical supposition that the object rotation center and the radiation source center intersect the image reconstruction center. In practice, the existing intersection deviation has influence on the image reconstruction precision. The image reconstruction mathematical model for shifted rotation center was established, and the relationship between the deviation error and reconstructed image precision was studied by simulation. As a result, for 512×512 CT reconstructed image, there is no distinctive difference between the reference image and the recon

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29

Ma, Chenxi. "Uncertainty-Aware GAN for Single Image Super Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4071–79. http://dx.doi.org/10.1609/aaai.v38i5.28201.

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Generative adversarial network (GAN) has become a popular tool in the perceptual-oriented single image super-resolution (SISR) for its excellent capability to hallucinate details. However, the performance of most GAN-based SISR methods is impeded due to the limited discriminative ability of their discriminators. In specific, these discriminators only focus on the global image reconstruction quality and ignore the more fine-grained reconstruction quality for constraining the generator, as they predict the overall realness of an image instead of the pixel-level realness. Here, we first introduce

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30

Rizvi, Saad, Jie Cao, Kaiyu Zhang, and Qun Hao. "Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning." Sensors 19, no. 19 (2019): 4190. http://dx.doi.org/10.3390/s19194190.

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Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image

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Zhao, Genping, Fei Li, Xiuwei Zhang, Kati Laakso, and Jonathan Cheung-Wai Chan. "Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection." Remote Sensing 13, no. 20 (2021): 4102. http://dx.doi.org/10.3390/rs13204102.

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Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreove

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Rankin, John, Fabio Muleri, Alessandro Di Marco, et al. "Equalizing the Pixel Response of the Imaging Photoelectric Polarimeter Onboard the IXPE Mission." Astronomical Journal 165, no. 5 (2023): 186. http://dx.doi.org/10.3847/1538-3881/acc38e.

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Abstract The Gas Pixel Detector is a gas detector, sensitive to the polarization of X-rays, currently flying onboard the Imaging X-ray Polarimetry Explorer (IXPE)—the first observatory dedicated to X-ray polarimetry. It detects X-rays and their polarization by imaging the ionization tracks generated by photoelectrons absorbed in the sensitive volume, and then reconstructing the initial direction of the photoelectrons. The primary ionization charge is multiplied and ultimately collected on a finely pixellated ASIC specifically developed for X-ray polarimetry. The signal of individual pixels is

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Yin, Zhye, Kedar Khare, and Bruno De Man. "Parametric boundary reconstruction algorithm for industrial CT metrology application." Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics 17, no. 2 (2009): 115–33. http://dx.doi.org/10.3233/xst-2009-021700217.

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High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by p

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Liang, Qi-Hang, Zi-Le Zhang, Xu-Kai Wang, Ya-Nan Zhao, and Su-Heng Zhang. "Single-pixel complex-amplitude imaging based on untrained complex-valued convolutional neural network." Optics Express 32, no. 17 (2024): 29656. http://dx.doi.org/10.1364/oe.532417.

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Single-pixel imaging is advancing rapidly in complex-amplitude imaging. However, reconstructing high-quality images demands significant acquisition and heavy computation, making the entire imaging process time-consuming. Here we propose what we believe to be a novel single-pixel complex-amplitude imaging (SCI) scheme using a complex-valued convolutional neural network for image reconstruction. The proposed sheme does not need to pre-train on any labeled data, and can quickly reconstruct high-quality complex-amplitude images with the randomly initialized network only under the constraints of th

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35

Jiang, Xinding, Ziyi Tong, Zhongyang Yu, et al. "Fourier Single-Pixel Imaging Based on Online Modulation Pattern Binarization." Photonics 10, no. 9 (2023): 963. http://dx.doi.org/10.3390/photonics10090963.

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Down-sampling Fourier single-pixel imaging is typically achieved by truncating the Fourier spectrum, where exclusively the low-frequency Fourier coefficients are extracted while discarding the high-frequency components. However, the truncation of the Fourier spectrum can lead to an undesired ringing effect in the reconstructed result. Moreover, the original Fourier single-pixel imaging necessitated grayscale Fourier basis patterns for illumination. This requirement limits imaging speed because digital micromirror devices (DMDs) generate grayscale patterns at a lower refresh rate. In order to s

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Kortelainen, Matti J., Martin Kwok, Taylor Childers, Alexei Strelchenko, and Yunsong Wang. "Porting CMS Heterogeneous Pixel Reconstruction to Kokkos." EPJ Web of Conferences 251 (2021): 03034. http://dx.doi.org/10.1051/epjconf/202125103034.

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Programming for a diverse set of compute accelerators in addition to the CPU is a challenge. Maintaining separate source code for each architecture would require lots of effort, and development of new algorithms would be daunting if it had to be repeated many times. Fortunately there are several portability technologies on the market such as Alpaka, Kokkos, and SYCL. These technologies aim to improve the developer’s productivity by making it possible to use the same source code for many different architectures. In this paper we use heterogeneous pixel reconstruction code from the CMS experimen

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Liu, Ruifeng, Shupeng Zhao, Pei Zhang, Hong Gao, and Fuli Li. "Complex wavefront reconstruction with single-pixel detector." Applied Physics Letters 114, no. 16 (2019): 161901. http://dx.doi.org/10.1063/1.5087094.

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Latorre-Carmona, Pedro, V. Javier Traver, J. Salvador Sánchez, and Enrique Tajahuerce. "Online reconstruction-free single-pixel image classification." Image and Vision Computing 86 (June 2019): 28–37. http://dx.doi.org/10.1016/j.imavis.2019.03.007.

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39

Dmitriev, E. A., and V. V. Myasnikov. "Possibility estimation of 3D scene reconstruction from multiple images." Information Technology and Nanotechnology, no. 2391 (2019): 293–96. http://dx.doi.org/10.18287/1613-0073-2019-2391-293-296.

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This paper presents a pixel-by-pixel possibility estimation of 3D scene reconstruction from multiple images. This method estimates conjugate pairs number with convolutional neural networks for further 3D reconstruction using classic approach. We considered neural networks that showed good results in semantic segmentation problem. The efficiency criterion of an algorithm is the resulting estimation accuracy. We conducted all experiments on images from Unity 3d program. The results of experiments showed the effectiveness of our approach in 3D scene reconstruction problem.

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40

Rueda-Chacón, Hoover Fabián, Cesar Augusto Vargas-García, and Henry Arguello-Fuentes. "Single-pixel optical sensing architecture for compressive hyperspectral imaging." Revista Facultad de Ingeniería Universidad de Antioquia, no. 73 (November 13, 2014): 134–43. http://dx.doi.org/10.17533/udea.redin.17312.

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Compressive hyperspectral imaging systems (CSI) capture the threedimensional (3D) information of a scene by measuring two-dimensional (2D) coded projections in a Focal Plane Array (FPA). These projections are then exploited by means of an optimization algorithm to obtain an estimation of the underlying 3D information. The quality of the reconstructions is highly dependent on the resolution of the FPA detector, which cost grows exponentially with the resolution. High-resolution low-cost reconstructions are thus desirable. This paper proposes a Single Pixel Compressive Hyperspectral Imaging Sens

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41

Freitas Carvalho, Francelino, Carlos Augusto de Moraes Cruz, Greicy Costa Marques, and Kayque Martins Cruz Damasceno. "Angular Light, Polarization and Stokes Parameters Information in a Hybrid Image Sensor with Division of Focal Plane." Sensors 20, no. 12 (2020): 3391. http://dx.doi.org/10.3390/s20123391.

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Targeting 3D image reconstruction and depth sensing, a desirable feature for complementary metal oxide semiconductor (CMOS) image sensors is the ability to detect local light incident angle and the light polarization. In the last years, advances in the CMOS technologies have enabled dedicated circuits to determine these parameters in an image sensor. However, due to the great number of pixels required in a cluster to enable such functionality, implementing such features in regular CMOS imagers is still not viable. The current state-of-the-art solutions require eight pixels in a cluster to dete

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42

Hong, Sungwook E., Sangnam Park, M. James Jee, Dongsu Bak, and Sangjun Cha. "Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network." Astrophysical Journal 923, no. 2 (2021): 266. http://dx.doi.org/10.3847/1538-4357/ac3090.

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Abstract We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on a convolutional neural network (CNN). Training data sets are generated with ray-tracing through cosmological simulations. We control the noise level of the galaxy shear catalog such that it mimics the typical properties of the existing ground-based WL observations of galaxy clusters. We find that the mass reconstruction by our multilayered CNN with the architecture of alternating convolution and trans-convolution filters significantly outperforms th

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Russ, John C. "Automatic vs computer-assisted 3D reconstruction." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 2 (1992): 1050–51. http://dx.doi.org/10.1017/s0424820100129887.

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Images from light or electron microscopy of serial sections must be aligned and corrected for distortion before use in three-dimensional reconstruction. As computer rendering supplants physical modelling, algorithms are desired for automatic alignment and rectification, requiring rotation and rubber-sheeting or warping. A new image can be constructed from each digitized original with polynomial equations relating the pixel coordinates in the new image to those in the original. Interpolation between the pixel values is used to prevent aliasing of edges. Calculating the equation coefficients req

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44

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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45

Rymarczyk, Tomasz, Edward Kozłowski, Grzegorz Kłosowski, and Konrad Niderla. "Logistic Regression for Machine Learning in Process Tomography." Sensors 19, no. 15 (2019): 3400. http://dx.doi.org/10.3390/s19153400.

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The main goal of the research presented in this paper was to develop a refined machine learning algorithm for industrial tomography applications. The article presents algorithms based on logistic regression in relation to image reconstruction using electrical impedance tomography (EIT) and ultrasound transmission tomography (UST). The test object was a tank filled with water in which reconstructed objects were placed. For both EIT and UST, a novel approach was used in which each pixel of the output image was reconstructed by a separately trained prediction system. Therefore, it was necessary t

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46

Feng, Jia, Qianxi Li, Jiawei Dong, Qing Zhao, and Hao Wang. "Single-Pixel Imaging Based on Enhanced Multi-Network Prior." Applied Sciences 15, no. 14 (2025): 7717. https://doi.org/10.3390/app15147717.

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Single-pixel imaging (SPI) is a significant branch of computational imaging. Owing to the high sensitivity, low cost, and wide spectrum, it acquires extensive applications across various domains. Nevertheless, multiple measurements and long reconstruction time constrain its application. The application of neural networks has significantly improved the quality of reconstruction, but there is still a huge space for improvement in performance. SAE and Unet have different advantages in the field of SPI. However, there is no method that combines the advantages of these two networks for SPI reconstr

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47

Sun, Ying, and Guang Lin Gao. "Study on Reconstruction Techniques of Landscape Image." Advanced Materials Research 1006-1007 (August 2014): 797–801. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.797.

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The depth map is a basic diagram of the intrinsic; each pixel value represents the scene graph the elevation position of the object point. In this paper, the analysis methods for target classification elevation map. Figure elevation are visible depth image, the depth of the image is the distance from each point in the scene to the image capture device values of the image as an image pixel value.

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Chen, Haozhen, Hancui Zhang, Bo Zou, and Long Wu. "Hybrid Self-Attention Transformer U-Net for Fourier Single-Pixel Imaging Reconstruction at Low Sampling Rates." Photonics 12, no. 6 (2025): 568. https://doi.org/10.3390/photonics12060568.

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Fourier Single-Pixel Imaging exhibits significant advantages over conventional imaging techniques, including high interference resistance, broad spectral adaptability, nonlocal imaging capability, and long-range detection. However, in practical applications, FSPI relies on undersampling reconstruction, which inevitably leads to ringing artifacts that degrade image quality. To enhance reconstruction performance, a Transformer-based FSPI reconstruction network is proposed. The network adopts a U-shaped architecture, composed of multiple Hybrid Self-Attention Transformer Modules and Feature Fusio

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Wang, Qingyu, Dihua Wu, Wei Liu, et al. "PlantStereo: A High Quality Stereo Matching Dataset for Plant Reconstruction." Agriculture 13, no. 2 (2023): 330. http://dx.doi.org/10.3390/agriculture13020330.

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Stereo matching is a depth perception method for plant phenotyping with high throughput. In recent years, the accuracy and real-time performance of the stereo matching models have been greatly improved. While the training process relies on specialized large-scale datasets, in this research, we aim to address the issue in building stereo matching datasets. A semi-automatic method was proposed to acquire the ground truth, including camera calibration, image registration, and disparity image generation. On the basis of this method, spinach, tomato, pepper, and pumpkin were considered for experime

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50

Rymarczyk, Tomasz, Grzegorz Kłosowski, Anna Hoła, et al. "Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms." Energies 14, no. 5 (2021): 1307. http://dx.doi.org/10.3390/en14051307.

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The article deals with the problem of detecting moisture in the walls of historical buildings. As part of the presented research, the following four methods based on mathematical modeling and machine learning were compared: total variation, least-angle regression, elastic net, and artificial neural networks. Based on the simulation data, the systems for the reconstruction of “pixel by pixel” tomographic images were trained. In order to test the reconstructive algorithms obtained during the research, images were generated based on real measurements and simulation cases. The method comparison wa

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