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Articles de revues sur le sujet « Super learning »

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Auteur : Grafiati
Publié le 6 juillet 2024

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1

Long, Jun, Jinhuan Zhang et Ping Du. « Super-sampling by learning-based super-resolution ». International Journal of Computational Science and Engineering 1, no 1 (2019) : 1. http://dx.doi.org/10.1504/ijcse.2019.10020177.

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Du, Ping, Jinhuan Zhang et Jun Long. « Super-sampling by learning-based super-resolution ». International Journal of Computational Science and Engineering 21, no 2 (2020) : 249. http://dx.doi.org/10.1504/ijcse.2020.105731.

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Haris, Muhammad, M. Rahmat Widyanto et Hajime Nobuhara. « Inception learning super-resolution ». Applied Optics 56, no 22 (21 juillet 2017) : 6043. http://dx.doi.org/10.1364/ao.56.006043.

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GURBYCH, A. « METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP ». Herald of Khmelnytskyi National University. Technical sciences 307, no 2 (2 mai 2022) : 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.

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This paper uses the Super Learning principle to predict the molecular affinity between the receptor (large biomolecule) and ligands (small organic molecules). Meta-models study the optimal combination of individual basic models in two consecutive ensembles – classification and regression. Each costume contains six models of machine learning, which are combined by stacking. Base models include the reference vector method, random forest, gradient boosting, neural graph networks, direct propagation, and transformers. The first ensemble predicts binding probability and classifies all candidate molecules to the selected receptor into active and inactive. Ligands recognized as involved by the first ensemble are fed to the second ensemble, which assumes the degree of their affinity for the receptor in the form of an inhibition factor (Ki). A feature of the method is the rejection of the use of atomic coordinates of individual molecules and their complexes – thus eliminating experimental errors in sample preparation and measurement of nuclear coordinates and the method to determine the affinity of biomolecules with unknown spatial configurations. It is shown that meta-learning increases the response (Recall) of the classification ensemble by 34.9% and the coefficient of determination (R2) of the regression ensemble by 21% compared to the average values. This paper shows that an ensemble with meta-stacking is an asymptotically optimal system for learning. The feature of Super Learning is to use k-fold cross-validation to form first-level predictions that teach second-level models — or meta-models — that combine first-level models optimally. The ability to predict the molecular affinity of six machine learning models is studied, and the efficiency improvement is due to the combination of models in the ensemble by the stacking method. Models that are combined into two consecutive ensembles are shown.
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Aitken, Michael R. F., Mark J. W. Larkin et Anthony Dickinson. « Super-learning of Causal Judgements ». Quarterly Journal of Experimental Psychology B 53, no 1 (1 février 2000) : 59–81. http://dx.doi.org/10.1080/027249900392995.

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Lim, Alane. « Machine learning method puts the “super” in super-resolution spectroscopy ». Scilight 2021, no 49 (3 décembre 2021) : 491108. http://dx.doi.org/10.1063/10.0009031.

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Han, Tong, Li Zhao et Chuang Wang. « Research on Super-resolution Image Based on Deep Learning ». International Journal of Advanced Network, Monitoring and Controls 8, no 1 (1 janvier 2023) : 58–65. http://dx.doi.org/10.2478/ijanmc-2023-0046.

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Abstract Image super-resolution is a kind of important image processing technology in computer vision and image processing. It refers to the process of recovering high-resolution image from low-resolution image. It has a wide range of real-world applications, such as medical imaging, security and others. In addition to improving image perception quality, it also helps improve other computer vision tasks. Compared with traditional methods, deep learning methods show better reconstruction results in the field of image super-resolution reconstruction, and have gradually developed into the mainstream technology. This article will study the depth in the super resolution direction is important method of types of introduction, combed the main image super-resolution reconstruction method, expounds the depth study of several important super-resolution network model, the advantages and disadvantages of different algorithms and adaptive application scenarios are analyzed and compared, this paper expounds the different ways in the super resolution to liquidate, Finally, the potential problems of current image super-resolution reconstruction techniques are discussed, and the future development direction is prospected.
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Jiang, Jingyu, Li Zhao et Yan Jiao. « Research on Image Super-resolution Reconstruction Based on Deep Learning ». International Journal of Advanced Network, Monitoring and Controls 7, no 1 (1 janvier 2022) : 1–21. http://dx.doi.org/10.2478/ijanmc-2022-0001.

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Abstract Image super-resolution reconstruction (SR) aims to use a specific algorithm to restore a low-resolution blurred image in the same scene into a high-resolution clear image. Due to its wide application value and theoretical value, image super-resolution reconstruction technology has become a research hotspot in the field of computer vision and image processing, and has attracted widespread attention from researchers. Compared with traditional methods, deep learning methods have shown better reconstruction effects in the field of image super-resolution reconstruction, and have gradually developed into the mainstream technology. Therefore, this paper classifies the image super-resolution reconstruction problem systematically according to the structure of the network model, and divides it into two categories: the super-division method based on the convolutional neural network model and the super-division method based on the generative confrontation network model. The main image super-resolution reconstruction methods are sorted out, several more important deep learning super-resolution reconstruction models are described, the advantages and disadvantages of different algorithms and the applicable application scenarios are analyzed and compared, and the different types of super-resolution algorithms are discussed. The method of mutual fusion and image and video quality evaluation, and a brief introduction to commonly used data sets. Finally, the potential problems faced by the current image super-resolution reconstruction technology are discussed, and a new outlook for the future development direction is made.
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Demontis, Ambra, Marco Melis, Battista Biggio, Giorgio Fumera et Fabio Roli. « Super-Sparse Learning in Similarity Spaces ». IEEE Computational Intelligence Magazine 11, no 4 (novembre 2016) : 36–45. http://dx.doi.org/10.1109/mci.2016.2601702.

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Strack, Rita. « Deep learning advances super-resolution imaging ». Nature Methods 15, no 6 (31 mai 2018) : 403. http://dx.doi.org/10.1038/s41592-018-0028-9.

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Kita, Koji, Michifumi Yoshioka, Katsufumi Inoue, Naru Inage et Shohei Tsunekawa. « Figure Patches Learning-based Super-Resolution ». IEEJ Transactions on Electronics, Information and Systems 136, no 7 (2016) : 929–37. http://dx.doi.org/10.1541/ieejeiss.136.929.

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Yang, Wenming, Fei Zhou, Rui Zhu, Kazuhiro Fukui, Guijin Wang et Jing-Hao Xue. « Deep learning for image super-resolution ». Neurocomputing 398 (juillet 2020) : 291–92. http://dx.doi.org/10.1016/j.neucom.2019.09.091.

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Wang, Wenjun, Chao Ren, Xiaohai He, Honggang Chen et Linbo Qing. « Video Super-Resolution via Residual Learning ». IEEE Access 6 (2018) : 23767–77. http://dx.doi.org/10.1109/access.2018.2829908.

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Yi Tang et Yuan Yuan. « Learning From Errors in Super-Resolution ». IEEE Transactions on Cybernetics 44, no 11 (novembre 2014) : 2143–54. http://dx.doi.org/10.1109/tcyb.2014.2301732.

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R. Mhatre, Sneha, et Jagdish W. Bakal. « A Review of Image Super Resolution using Deep Learning ». International Journal on Recent and Innovation Trends in Computing and Communication 11, no 5s (17 mai 2023) : 145–49. http://dx.doi.org/10.17762/ijritcc.v11i5s.6638.

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The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designed, image enhancement used or not, among other factors. To improve the accuracy of the image super resolution, a different deep learning techniques might be explored. In fact, greater research into the image super resolution in medical imaging is possible to improve the data's suitability for future analysis. In light of this, it can be said that there is a lot of scope for research in the field of medical imaging.
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Singh, Kajol, et Manish Saxena. « A Review on Medical Image Super Resolution with Application of Deep Learning ». SMART MOVES JOURNAL IJOSCIENCE 7, no 2 (27 mars 2021) : 25–29. http://dx.doi.org/10.24113/ijoscience.v7i2.368.

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Super resolution problems are often discussed in medical imaging. The spatial resolution of medical images is insufficient due to limitations such as image acquisition time, low radiation dose or hardware limitations. Various super-resolution methods have been proposed to solve these problems, such as optimization or learning-based approaches. Recently, deep learning methodologies have become a thriving technology and are evolving at an exponential rate. We believe we need to write a review to illustrate the current state of deep learning in super-resolution medical imaging. In this article, we provide an overview of image resolution and the deep learning introduced in super resolution. This document describes super resolution for single images versus super resolution for multiple images, evaluation metrics and loss functions.
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He, H., K. Gao, W. Tan, L. Wang, S. N. Fatholahi, N. Chen, M. A. Chapman et J. Li. « IMPACT OF DEEP LEARNING-BASED SUPER-RESOLUTION ON BUILDING FOOTPRINT EXTRACTION ». International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (30 mai 2022) : 31–37. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-31-2022.

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Abstract. Automated building footprints extraction from High Spatial Resolution (HSR) remote sensing images plays important roles in urban planning and management, and hazard and disease control. However, HSR images are not always available in practice. In these cases, super-resolution, especially deep learning (DL)-based methods, can provide higher spatial resolution images given lower resolution images. In a variety of remote sensing applications, DL based super-resolution methods are widely used. However, there are few studies focusing on the impact of DL-based super-resolution on building footprint extraction. As such, we present an exploration of this topic. Specifically, we first super-resolve the Massachusetts Building Dataset using bicubic interpolation, a pre-trained Super-Resolution CNN (SRCNN), a pre-trained Residual Channel Attention Network (RCAN), a pre-trained Residual Feature Aggregation Network (RFANet). Then, using the dataset under its original resolution, as well as the four different super-resolutions of the dataset, we employ the High-Resolution Network (HRNet) v2 to extract building footprints. Our experiments show that super-resolving either training or test datasets using the latest high-performance DL-based super-resolution method can improve the accuracy of building footprints extraction. Although SRCNN based building footprint extraction gives the highest Overall Accuracy, Intersection of Union and F1 score, we suggest using the latest super-resolution method to process images before building footprint extraction due to the fixed scale ratio of pre-trained SRCNN and low speed of convergence in training.
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Liu, Huanyu, Jiaqi Liu, Junbao Li, Jeng-Shyang Pan et Xiaqiong Yu. « DL-MRI : A Unified Framework of Deep Learning-Based MRI Super Resolution ». Journal of Healthcare Engineering 2021 (9 avril 2021) : 1–9. http://dx.doi.org/10.1155/2021/5594649.

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Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.
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Pllana, Duli. « Combining Teaching Strategies, Learning Strategies, and Elements of Super Learning Principles ». Advances in Social Sciences Research Journal 8, no 6 (27 juin 2021) : 288–301. http://dx.doi.org/10.14738/assrj.86.10366.

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Blending teaching strategies, learning strategies, and elements of super learning principles increase learning outcomes tremendously in any case, situation, or academic subject. Employing teaching and learning strategies adequately impact on an interactive session (academic subjects or any field) to a great degree, enhance learners’ motivation significantly, improve self confidence and self esteem of learners considerably, and soar learning outcomes substiantly. It is impossible to combine all learning and teaching strategies (there are many techniques, and a small space time to incorporate them in one lesson or an academic subject.) in an academic subject entirely. Accordingly, strategic teaching or learning establishes skills or techniques in addressing a lesson or digesting information from the lesson. Also, learning results depend on the quantity and quality of combining learning and teaching strategies, and components of super learning principles. The greater the participation of mixing techniques or skills in a lesson, the greater are the positive results in the learning outcomes. Teaching and learning strategies, and superlearning elements are in a close relationship with each other; teaching strategies imply learning strategies and elements of super learning. Combination of the three ingredients play a crucial part in any lesson, academic subject, or general knowledge; mixing all these three components together wisely maximizes learning outcomes enormously.
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Ordyniak, S., et S. Szeider. « Parameterized Complexity Results for Exact Bayesian Network Structure Learning ». Journal of Artificial Intelligence Research 46 (5 mars 2013) : 263–302. http://dx.doi.org/10.1613/jair.3744.

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Bayesian network structure learning is the notoriously difficult problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian network structure learning under graph theoretic restrictions on the (directed) super-structure. The super-structure is an undirected graph that contains as subgraphs the skeletons of solution networks. We introduce the directed super-structure as a natural generalization of its undirected counterpart. Our results apply to several variants of score-based Bayesian network structure learning where the score of a network decomposes into local scores of its nodes. Results: We show that exact Bayesian network structure learning can be carried out in non-uniform polynomial time if the super-structure has bounded treewidth, and in linear time if in addition the super-structure has bounded maximum degree. Furthermore, we show that if the directed super-structure is acyclic, then exact Bayesian network structure learning can be carried out in quadratic time. We complement these positive results with a number of hardness results. We show that both restrictions (treewidth and degree) are essential and cannot be dropped without loosing uniform polynomial time tractability (subject to a complexity-theoretic assumption). Similarly, exact Bayesian network structure learning remains NP-hard for "almost acyclic" directed super-structures. Furthermore, we show that the restrictions remain essential if we do not search for a globally optimal network but aim to improve a given network by means of at most k arc additions, arc deletions, or arc reversals (k-neighborhood local search).
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Jian, Zhang, Xu Tengteng, Qian Jianjun, Yuchen Xiao, Heng Zhang, Hongran Li et Cunhua Li. « Single Image Self-Learning Super-Resolution with Robust Matrix Regression ». AATCC Journal of Research 8, no 1_suppl (septembre 2021) : 135–42. http://dx.doi.org/10.14504/ajr.8.s1.17.

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The similarity measure plays the key role in the self-learning framework for single image super-resolution. This paper involves matrix regression with properties of robustness and two-dimensional structure to measure the similarity between image blocks and enhance the effect of super-resolution. Specifically, we use the minimal nuclear norm of representation error as a criterion, and the alternating direction method of multipliers (ADMM) to calculate the similarity between high- and low-resolution image blocks. Evaluation on several images with different interference and experimental results of super-resolution images clearly demonstrate the advantages of our proposed method in visual robustness and super-resolution effects.
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Lin, Xu, Qingqing Zhang, Hongyue Wang, Chaolong Yao, Changxin Chen, Lin Cheng et Zhaoxiong Li. « A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning ». Remote Sensing 14, no 9 (2 mai 2022) : 2181. http://dx.doi.org/10.3390/rs14092181.

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The study of digital elevation model (DEM) super-resolution reconstruction algorithms has solved the problem of the need for high-resolution DEMs. However, the DEM super-resolution reconstruction algorithm itself is an inverse problem, and making full use of the DEM a priori information is an effective way to solve this problem. In our work, a new DEM super-resolution reconstruction method is proposed based on the complementary relationship between internally learned super-resolution reconstruction methods and externally learned super-resolution reconstruction methods. The method is based on the presence of a large amount of repetitive information within the DEM. Using an internal learning approach to learn the internal prior of the DEM, a low-resolution dataset of the DEM rich in detailed features is generated, and based on this, the training of a constrained external learning network is constructed for the discrepancy data pair. Finally, it introduces residual learning based on the network model to accelerate the operation rate of the network and to solve the model degradation problem brought about by the deepening of the network. This enables the better transfer of learned detailed features in deeper network mappings, which in turn ensures accurate learning of the DEM prior information. The network utilizes the internal prior of the specific DEM as well as the external prior of the DEM dataset and achieves better super-resolution reconstruction results in the experimental results. The results of super-resolution reconstruction by the Bicubic method, Super-Resolution Convolutional Neural Networks (SRCNN), very deep convolutional networks (VDSR), ”Zero-Shot” Super-Resolution networks (ZSSR) and the new method in this paper were compared, and the average RMSE of the super-resolution reconstruction results of the five methods were 8.48 m, 8.30 m, 8.09 m, 7.02 m and 6.65 m, respectively. The mean elevation error at the same resolution is 21.6% better than that of the Bicubic method, 19.9% better than that of the SRCNN, 17.8% better than that of the VDSR method, and 5.3% better than that of the ZSSR method.
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Maftuh, Muhammad Kholidin, et Dayat Hidayat. « THE EFFECT OF SUPERITEM LEARNING MODEL ON INCREASING STUDENTs LEARNING ACHIEVEMENTS ». (JIML) JOURNAL OF INNOVATIVE MATHEMATICS LEARNING 1, no 4 (28 novembre 2018) : 367. http://dx.doi.org/10.22460/jiml.v1i4.p367-373.

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This study aims to see the influence of super-learning models on student achievement. This research is rarely done because of the lack of literature. In learning mathematics, it should not be direct to complex or complex concepts, but it must start from a simple concept. The aim to be achieved in this study is to determine whether or not the influence of super-learning learning models on quadrilateral learning on improving student learning achievement. The population in this study were Pangkalan 2 junior high school students. Samples were taken using makeshift sample techniques where the entire population was sampled; one class was made into the experimental class namely class VII A; and one class was made into the control class namely class VII B. The method in this study was experimental method. There contained research on students of SMPN 2 Pangkalan Karawang regency class VII with quadrilateral. The instruments in this study were Student Worksheets and achievement tests given at the initial test and final test. Based on the results of the analysis and testing of hypotheses, it was concluded that the use of Superitem learning models had a positive effect on improving student learning achievement, and there were differences in the increase in learning achievement between students who had mathematical learning using super-learning models with students who used mathematics learning conventional learning.
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Davies, Molly Margaret, et Mark J. van der Laan. « Optimal Spatial Prediction Using Ensemble Machine Learning ». International Journal of Biostatistics 12, no 1 (1 mai 2016) : 179–201. http://dx.doi.org/10.1515/ijb-2014-0060.

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Abstract Spatial prediction is an important problem in many scientific disciplines. Super Learner is an ensemble prediction approach related to stacked generalization that uses cross-validation to search for the optimal predictor amongst all convex combinations of a heterogeneous candidate set. It has been applied to non-spatial data, where theoretical results demonstrate it will perform asymptotically at least as well as the best candidate under consideration. We review these optimality properties and discuss the assumptions required in order for them to hold for spatial prediction problems. We present results of a simulation study confirming Super Learner works well in practice under a variety of sample sizes, sampling designs, and data-generating functions. We also apply Super Learner to a real world dataset.
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He, Yifan, Wei Cao, Xiaofeng Du et Changlin Chen. « Internal Learning for Image Super-Resolution by Adaptive Feature Transform ». Symmetry 12, no 10 (14 octobre 2020) : 1686. http://dx.doi.org/10.3390/sym12101686.

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Recent years have witnessed the great success of image super-resolution based on deep learning. However, it is hard to adapt a well-trained deep model for a specific image for further improvement. Since the internal repetition of patterns is widely observed in visual entities, internal self-similarity is expected to help improve image super-resolution. In this paper, we focus on exploiting a complementary relation between external and internal example-based super-resolution methods. Specifically, we first develop a basic network learning external prior from large scale training data and then learn the internal prior from the given low-resolution image for task adaptation. By simply embedding a few additional layers into a pre-trained deep neural network, the image-adaptive super-resolution method exploits the internal prior for a specific image, and the external prior from a well-trained super-resolution model. We achieve 0.18 dB PSNR improvements over the basic network’s results on standard datasets. Extensive experiments under image super-resolution tasks demonstrate that the proposed method is flexible and can be integrated with lightweight networks. The proposed method boosts the performance for images with repetitive structures, and it improves the accuracy of the reconstructed image of the lightweight model.
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Li, Xiaoyan, Lefei Zhang et Jane You. « Domain Transfer Learning for Hyperspectral Image Super-Resolution ». Remote Sensing 11, no 6 (22 mars 2019) : 694. http://dx.doi.org/10.3390/rs11060694.

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A Hyperspectral Image (HSI) contains a great number of spectral bands for each pixel; however, the spatial resolution of HSI is low. Hyperspectral image super-resolution is effective to enhance the spatial resolution while preserving the high-spectral-resolution by software techniques. Recently, the existing methods have been presented to fuse HSI and Multispectral Images (MSI) by assuming that the MSI of the same scene is required with the observed HSI, which limits the super-resolution reconstruction quality. In this paper, a new framework based on domain transfer learning for HSI super-resolution is proposed to enhance the spatial resolution of HSI by learning the knowledge from the general purpose optical images (natural scene images) and exploiting the cross-correlation between the observed low-resolution HSI and high-resolution MSI. First, the relationship between low- and high-resolution images is learned by a single convolutional super-resolution network and then is transferred to HSI by the idea of transfer learning. Second, the obtained Pre-high-resolution HSI (pre-HSI), the observed low-resolution HSI, and high-resolution MSI are simultaneously considered to estimate the endmember matrix and the abundance code for learning the spectral characteristic. Experimental results on ground-based and remote sensing datasets demonstrate that the proposed method achieves comparable performance and outperforms the existing HSI super-resolution methods.
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Leli, Vito M., Saeed Osat, Timur Tlyachev, Dmitry V. Dylov et Jacob D. Biamonte. « Deep learning super-diffusion in multiplex networks ». Journal of Physics : Complexity 2, no 3 (10 juin 2021) : 035011. http://dx.doi.org/10.1088/2632-072x/abe6e9.

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Heo, Bo-Young, et Byung Cheol Song. « Learning-based Super-resolution for Text Images ». Journal of the Institute of Electronics and Information Engineers 52, no 4 (25 avril 2015) : 175–83. http://dx.doi.org/10.5573/ieie.2015.52.4.175.

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Singh, Nisha, et Myna A.N. « Image Super-Resolution Using Deep Learning Technique ». International Journal of Computer Sciences and Engineering 6, no 7 (31 juillet 2018) : 150–55. http://dx.doi.org/10.26438/ijcse/v6i7.150155.

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Chae, Byungjoo, Jinsun Park, Tae-Hyun Kim et Donghyeon Cho. « Online Learning for Reference-Based Super-Resolution ». Electronics 11, no 7 (28 mars 2022) : 1064. http://dx.doi.org/10.3390/electronics11071064.

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Online learning is a method for exploiting input data to update deep networks in the test stage to derive potential performance improvement. Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR) image for the online adaptation of deep networks. Unlike SISR approaches, reference-based super-resolution (RefSR) algorithms benefit from an additional high-resolution (HR) reference image containing plenty of useful features for enhancing the input LR image. Therefore, we introduce a new online learning algorithm, using several reference images, which is applicable to not only RefSR but also SISR networks. Experimental results show that our online learning method is seamlessly applicable to many existing RefSR and SISR models, and that improves performance. We further present the robustness of our method to non-bicubic degradation kernels with in-depth analyses.
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Qin, Yu, Yuxing Li, Zhizheng Zhuo, Zhiwen Liu, Yaou Liu et Chuyang Ye. « Multimodal super-resolved q-space deep learning ». Medical Image Analysis 71 (juillet 2021) : 102085. http://dx.doi.org/10.1016/j.media.2021.102085.

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Chen, Chaofeng, Dihong Gong, Hao Wang, Zhifeng Li et Kwan-Yee K. Wong. « Learning Spatial Attention for Face Super-Resolution ». IEEE Transactions on Image Processing 30 (2021) : 1219–31. http://dx.doi.org/10.1109/tip.2020.3043093.

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Kawulok, Michal, Pawel Benecki, Szymon Piechaczek, Krzysztof Hrynczenko, Daniel Kostrzewa et Jakub Nalepa. « Deep Learning for Multiple-Image Super-Resolution ». IEEE Geoscience and Remote Sensing Letters 17, no 6 (juin 2020) : 1062–66. http://dx.doi.org/10.1109/lgrs.2019.2940483.

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Jiang, Zhuqing, Honghui Zhu, Yue Lu, Guodong Ju et Aidong Men. « Lightweight Super-Resolution Using Deep Neural Learning ». IEEE Transactions on Broadcasting 66, no 4 (décembre 2020) : 814–23. http://dx.doi.org/10.1109/tbc.2020.2977513.

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Kumar, Neeraj, et Amit Sethi. « Fast Learning-Based Single Image Super-Resolution ». IEEE Transactions on Multimedia 18, no 8 (août 2016) : 1504–15. http://dx.doi.org/10.1109/tmm.2016.2571625.

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Huang, Weiqin, Xiaorui Li, Yikai Gu, Xiaofu Du et Xiancheng Zhu. « Learning Enriched Features for Image Super Resolution ». IEEE Access 10 (2022) : 113583–97. http://dx.doi.org/10.1109/access.2022.3216672.

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Tang, Yi, Pingkun Yan, Yuan Yuan et Xuelong Li. « Single-image super-resolution via local learning ». International Journal of Machine Learning and Cybernetics 2, no 1 (12 février 2011) : 15–23. http://dx.doi.org/10.1007/s13042-011-0011-6.

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Shamsolmoali, Pourya, Abdul Hamid Sadka, Huiyu Zhou et Wankou Yang. « Advanced deep learning for image super-resolution ». Signal Processing : Image Communication 82 (mars 2020) : 115732. http://dx.doi.org/10.1016/j.image.2019.115732.

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Naimi, Ashley I., et Laura B. Balzer. « Stacked generalization : an introduction to super learning ». European Journal of Epidemiology 33, no 5 (10 avril 2018) : 459–64. http://dx.doi.org/10.1007/s10654-018-0390-z.

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Chaudhari, Akshay S., Zhongnan Fang, Feliks Kogan, Jeff Wood, Kathryn J. Stevens, Eric K. Gibbons, Jin Hyung Lee, Garry E. Gold et Brian A. Hargreaves. « Super‐resolution musculoskeletal MRI using deep learning ». Magnetic Resonance in Medicine 80, no 5 (26 mars 2018) : 2139–54. http://dx.doi.org/10.1002/mrm.27178.

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Hasan, Zahraa. « Deep Learning for Super Resolution and Applications ». Galoitica : Journal of Mathematical Structures and Applications 8, no 2 (2023) : 34–42. http://dx.doi.org/10.54216/gjmsa.080204.

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High-resolution technologies are aimed at obtaining a high-resolution image from a low-resolution image, and the importance of this field has increased due to the emergence of the need to have high-resolution images in many important applications such as medical, security, and other images. Methods for obtaining ultra-high-resolution images have developed after the advent of Deep Learning Technologies, which have shown good results in this task, Due to the importance of the field of ultra-high-resolution images and deep learning, In this article we will explain one of the deep learning models used to obtain a high-resolution image from a low-resolution image and how to build and train it based on one of the famous deep learning offices and using one of the google platforms used in training, namely Google Laboratory
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Yang, Guangtong, Chen Li, Yudong Yao, Ge Wang et Yueyang Teng. « Quasi-supervised learning for super-resolution PET ». Computerized Medical Imaging and Graphics 113 (avril 2024) : 102351. http://dx.doi.org/10.1016/j.compmedimag.2024.102351.

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Geiss, Andrew, Sam J. Silva et Joseph C. Hardin. « Downscaling atmospheric chemistry simulations with physically consistent deep learning ». Geoscientific Model Development 15, no 17 (5 septembre 2022) : 6677–94. http://dx.doi.org/10.5194/gmd-15-6677-2022.

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Abstract. Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrates the downscaling capabilities of modern CNN-based single image super resolution and video super-resolution schemes and develops modifications to these schemes to ensure they are appropriate for use with physical science data. The CNN-based video super-resolution schemes in particular incur only 39 % to 54 % of the grid-cell-level error of interpolation schemes and generate outputs with extremely realistic small-scale variability based on multiple perceptual quality metrics while performing a large (8×10) increase in resolution in the spatial dimensions. Methods are introduced to strictly enforce physical conservation laws within CNNs, perform large and asymmetric resolution changes between common model grid resolutions, account for non-uniform grid-cell areas, super-resolve lognormally distributed datasets, and leverage additional inputs such as high-resolution climatologies and model state variables. High-resolution chemistry simulations are critical for modeling regional air quality and for understanding future climate, and CNN-based downscaling has the potential to generate these high-resolution simulations and ensembles at a fraction of the computational cost.
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Wu, Haozhe. « Super-Resolution of Lightweight Images Based on Deep Learning ». Highlights in Science, Engineering and Technology 81 (26 janvier 2024) : 456–60. http://dx.doi.org/10.54097/f8y87181.

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As digital imaging technology advances, a significant breakthrough is the emergence of super-resolution technology, a method to enhance the quality of low-resolution images to high-resolution. When there are some developing of the newest digital camera's skills, the super-resolution technology is appearing and getting more and more importance. In the simplest terms, the image super-resolution skill is the technology of which one cover the process from low resolution images to high resolution images skills. This technology differs from the traditional approach in that it is attracting more and more attention from researchers and practitioners. This method is important in the future. With the passage of time, it will be more and more mature. In this paper, we focus on how to improve image resolution by using various technologies, and summarise the results obtained in recent years. Then, we briefly introduce the background and development of super resolution, then briefly introduce the technology. Finally, we come to a few conclusions regarding Deep Learning.
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Dewi, Ratna Kumala. « INNOVATION OF BIOCHEMISTRY LEARNING IN WELCOMING THE SUPER SMART SOCIETY 5.0 ERA ». INSECTA : Integrative Science Education and Teaching Activity Journal 2, no 2 (29 novembre 2021) : 197–208. http://dx.doi.org/10.21154/insecta.v2i2.3507.

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TTechnological developments are now in the era of super-smart society 5.0 which is an advanced solution to the 4.0 industrial revolution. The era of super-smart society 5.0 is a learning innovation that changes from basic literacy to digital literacy. This study aims to analyze the innovation of Biochemistry learning in the Department of Chemistry at UIN Sayyid Ali Rahmatullah Tulungagung in welcoming the era of super-smart society 5.0. The research method used descriptive qualitative. The data analysis technique was carried out using a literature review. The method of data collection was carried out by observation, distributing questionnaires, documentation, lecturer interviews, and student interviews. The research instrument consisted of observation sheets, questionnaires, and interview sheets. The results showed that in the era of super-smart society 5.0, lecturers and students were required to be quick in making decisions and solutions when learning Biochemistry. Lecturers must dig up information and look for innovations so that students can think ahead and keep up with the times according to the era of super-smart society 5.0. Lecturers act as tutors or teachers, facilitators, and inspire students to achieve learning objectives. Based on the results of the study, it can be concluded that lecturers must have the ability in digital literacy and train students to be able to think critically and creatively in learning Biochemistry in the era of super-smart society 5.0.
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Liu, Ding, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Xinchao Wang et Thomas S. Huang. « Learning Temporal Dynamics for Video Super-Resolution : A Deep Learning Approach ». IEEE Transactions on Image Processing 27, no 7 (juillet 2018) : 3432–45. http://dx.doi.org/10.1109/tip.2018.2820807.

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Yue, Bo, Shuang Wang, Xuefeng Liang et Licheng Jiao. « An external learning assisted self-examples learning for image super-resolution ». Neurocomputing 312 (octobre 2018) : 107–19. http://dx.doi.org/10.1016/j.neucom.2018.05.076.

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Yu, Li, Yunpeng Ma, Song Hong et Ke Chen. « Reivew of Light Field Image Super-Resolution ». Electronics 11, no 12 (17 juin 2022) : 1904. http://dx.doi.org/10.3390/electronics11121904.

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Currently, light fields play important roles in industry, including in 3D mapping, virtual reality and other fields. However, as a kind of high-latitude data, light field images are difficult to acquire and store. Thus, the study of light field super-resolution is of great importance. Compared with traditional 2D planar images, 4D light field images contain information from different angles in the scene, and thus the super-resolution of light field images needs to be performed not only in the spatial domain but also in the angular domain. In the early days of light field super-resolution research, many solutions for 2D image super-resolution, such as Gaussian models and sparse representations, were also used in light field super-resolution. With the development of deep learning, light field image super-resolution solutions based on deep-learning techniques are becoming increasingly common and are gradually replacing traditional methods. In this paper, the current research on super-resolution light field images, including traditional methods and deep-learning-based methods, are outlined and discussed separately. This paper also lists publicly available datasets and compares the performance of various methods on these datasets as well as analyses the importance of light field super-resolution research and its future development.
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Masihu, Junardin Muhamad, et Edi Masihu. « Application of Super Item Learning Model in Improving Learning Outcomes of Photosynthesis Concept in Class VIII of SMP Al-Wathan Ambon ». PEDAGOGIC : Indonesian Journal of Science Education and Technology 1, no 2 (1 décembre 2022) : 72–86. http://dx.doi.org/10.54373/ijset.v2i1.55.

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The super item learning model is one of the learning models that is oriented towards understanding and activeness of students in problem solving. This type of research is descriptive with the aim of determining whether the application of the Super ITEM model in improving the learning outcomes of the concept of photosynthesis in class VIII2 of SMP AL-Wathan Ambon. The sample in this study was 29 people. The instruments used are tests, affective observation sheets, and psychomotor observation sheets. The data analysis used is descriptive data analysis of student learning outcomes, affective observation results, and student psychomotor. The results of data analysis obtained the learning process using a super item learning model students are more active in the learning process when compared to the learning process that is carried out conventionally. These results can be seen from the classical average score obtained, namely in the initial test implementation, the average score obtained was 47.37 and increased in the implementation of the final test, whose average score obtained was 75.40. Based on the results of learning completion using a super item learning model, students who achieved a minimum completion standard of 23 people or with a percentage of 79.31 percent.
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Bhujade, Rakesh Kumar, et Stuti Asthana. « An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution ». International Journal of Emerging Technology and Advanced Engineering 12, no 11 (1 novembre 2022) : 153–58. http://dx.doi.org/10.46338/ijetae1122_16.

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The image up scaling and super resolution gain a attention now a days. Create a high-resolution image with help of a low-resolution image uses in many of the application. The optimization based super-resolution methods is principally driven by the choice of the objective function. Recently artificial intelligence based machine and deep learning methods are also using for the image processing application. It makes easy and efficient way of image super resolution. Recent work has generally centered around limiting the mean squared reproduction blunder. The subsequent evaluations have high pinnacle signal-tocommotion proportions, however they are much of the time lacking high-recurrence subtleties and are perceptually subpar as in they neglect to match the constancy expected at the higher resolution. This paper presents the comparative analysis of the efficient techniques for image super-resolution. Keywords—Image, Super, Resolution, AI, Machine learning, Deep Learning, Perfromance.
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