He has been appointed to invited visiting professorships at several Universities and Research and Innovation Centres, including at Anhui University (China) and Taibah Valley (Taibah University, Saudi Arabia). In Proceedings of the 15th European Conference on Computer Vision, Springer, Munich, Germany, pp. Valentin Masero received the B. Eng. DOI: https://doi.org/10.1109/38.988747. Copyright 2022 ACM, Inc. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods, Survey of hyperspectral earth observation applications from space in the sentinel-2 context, Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission, Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation, Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study, Remote sensing and mapping of Tamarisk along the Colorado river, USA: a comparative use of summer-acquired Hyperion, Thematic Mapper and QuickBird data, Extending the vegetationimpervioussoil model using simulated EnMAP data and machine learning, The potential of pan-sharpened EnMAP data for the assessment of wheat LAI, Potential of resolution-enhanced hyperspectral data for mineral mapping using simulated EnMAP and Sentinel-2 images, QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management, Review ArticleDigital change detection methods in ecosystem monitoring: a review, First hyperspectral imaging survey of the deep seafloor: High-resolution mapping of manganese nodules, Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data. A novel framework to train a deep neural network where the SR sub-network explicitly incorporates a detection loss in its training objective, via a tradeoff with a traditional detection loss is proposed. Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution. Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. B. Gao. Y. Tai, J. Yang, X. M. Liu, C. Y. Xu. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. In this exposition, we extensively compare 30+ state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence. degree in electrical and electronics from Wollongong University, Australia in 2012. Anchored neighborhood regression for fast example-based super-resolution. In Proceedings the 8th IEEE International Conference on Computer Vision, IEEE, Vancouver, Canada, 2001. DOI: https://doi.org/10.1109/CVPRW.2018.00131. This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. 1 Deep Learning for Image Super-resolution: A Survey. W. T. Freeman, E. C. Pasztor, O. T. Carmichael. We propose a deep learning method for single image super-resolution (SR). Experimental results demonstrate that the proposed networks successfully incorporate the 3D geometric information and super-resolve the texture maps. This survey presents a deep review of the literature designed for students and professionals who want to know more about the topic. A+: Adjusted anchored neighborhood regression for fast super-resolution. Y. F. Wang, F. Perazzi, B. McWilliams, A. Sorkine-Hornung, O. Sorkine-Hornung, C. Schroers. MATH He received the Ph. MATH This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super- resolution. Water body extraction from Sentinel-3 image with multiscale spatiotemporal super-resolution mapping, Super-resolution Imaging of the Protoplanetary Disk HD 142527 Using Sparse Modeling, High-resolution mid-infrared imaging of the asymptotic giant branch star rv bootis with the steward observatory adaptive optics system. 7, no. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Deep Unfolding Network for Image Super-Resolution Abstract: Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. EnhanceNet: Single image super-resolution through automated texture synthesis. This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. 8, pp. DOI: https://doi.org/10.1109/CVPR.2018.00262. In this survey, we aim to give a survey on recent advances of image super . Jiang J., Ma J., Image fusion meets deep learning: A survey and . 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 31183126, 2018. Deep laplacian pyramid networks for fast and accurate super-resolution. 40, no. In Proceedings of the 9th International Conference on Brain Inspired Cognitive Systems, Springer, Xian, China, pp. M. Haris, G. Shakhnarovich, N. Ukita. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research. 561568, 2013. Park, H. Son, S. Cho, K. S. Hong, S. Lee. Image super-resolution via deep recursive residual network. The authors would like acknowledge the support from the Shanxi Hundred People Plan of China and colleagues from the Image Processing Group in Strathclyde University (UK), Anhui University (China) and Taibah Valley (Taibah University, Saudi Arabia) respectively, for their valuable suggestions. 58355843, 2017. Super-resolution through neighbor embedding. The mapping is represented as a deep, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). In general . View 5 excerpts, references methods and background, 2015 IEEE International Conference on Computer Vision (ICCV). Normally, pairs of high-resolution and low-resolution images are used to train the deep learning models. Abstract. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, pp. Y. L. Zhang, K. P. Li, K. Li, L. C. Wang, B. N. Zhong, Y. Fu. 16641673, 2018. MemNet: A persistent memory network for image restoration. The gray board denotes the coordinates of pixels, and the blue, yellow and green points represent the initial, intermediate and final pixels, respectively. D. degrees from the University of Strathclyde in Glasgow, UK, in 1992 and 1997, respectively. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Venice, Italy, pp. Towards Principled Methods for Training Generative Adversarial Networks, [Online], Available: https://arxiv.org/abs/1701.04862, April 8, 2018. We introduce a taxonomy for deep-learning based . . He is a member of the Chinese Computer Society, and has been a visiting scholar in Department of Computer Science, San Jose State University, USA. DOI: https://doi.org/10.1109/TIP.2010.2050625. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, vol. Deep learning can be used to efficiently learn pixel-level features in image data by simulating human behavior after sensing information through its hierarchical structure and can be used to continuously optimize structural parameters for better performance through end-to-end data-driven learning. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, UK, Viet Khanh Ha,Jin-Chang Ren&Sophia Zhao, College of Information Engineering, Taiyuan University of Technology, Taiyuan, 030024, China, School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China, Department of Computer Systems and Telematics Engineering, University of Extremadura, Badajoz, 06006, Spain, School of Computing, Edinburgh Napier University, Edinburgh, EH10 5DT, UK, School of Computer Science and Technology, Anhui University, Anhui, 230039, China, You can also search for this author in Image super-resolution via sparse representation. E. Prez-Pellitero, J. Salvador, J. Ruiz-Hidalgo, B. Rosenhahn. International Journal of Computer Vision, vol. 11321140, 2017. His research interests include image super resolution using deep learning. DOI: https://doi.org/10.1109/CVPR.2017.618. and Ph. Accelerating the Super-Resolution Convolutional Neural Network. To overcome this, a wide range of related mechanisms has been introduced into the SR networks . Abstract. 26722680, 2014. X. L. Wang, R. Girshick, A. Gupta, K. M. He. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, USA, 2004. Generative adversarial nets. Fig. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Venice, Italy, pp. The learning-based methods have recently . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, vol. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. His research interests include image processing, machine learning, artificial intelligence, computer graphics, computer programming, software development, computer applications in industrial engineering, computer applications in agricultural engineering and computer applications in healthcare. Xin-Ying Xu received the B. Sc. 694711, 2016. W. T. Freeman, T. R. Jones, E. C. Pasztor. DOI: https://doi.org/10.1007/978-3-030-01249-6_16. Against this backdrop, the broad success of deep learning (DL) has prompted the . Zhihao Wang, Jian Chen, Steven C.H. A cascaded convolution neural network for image super-resolution (CSRCNN), which includes three cascaded Fast SRCNNs and each Fast S RCNN can process a specific scale image. 25692582, 2014. IEEE Computer Graphics and Applications, vol. M. Arjovsky, L. Bottou. 2, pp. IEEE Int. DOI: https://doi.org/10.1023/A:1026501619075. Abstract. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27902798, 2017. Learn more. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, pp. C. Dong, C. C. Loy, K. M. He, X. O. Tang. Single image super-resolution from transformed self-exemplars. The most recent deep learning based SISR methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and high-resolution (HR) outputs. 3,581 Highly Influential PDF View 8 excerpts, references methods 295307, 2016. S. Schulter, C. Leistner, H. Bischof. 45014510, 2017. DOI: https://doi.org/10.1007/978-3-319-70096-0_23. It is clearly expressed in the concept that the artificial neural network model can extract and learn the features of the original data through multi-layer nonlinear. Fast and accurate image upscaling with super-resolution forests. This survey aims to review deep learning-based image super-resolution methods, including Convolutional Neural Networks and Generative Adversarial Networks based on internal network structure, and describes the applications of single-frame image super resolution in various practical fields. 16371645, 2016. Joint sub-bands learning with clique structures for wavelet domain super-resolution. His research interests include computational intelligence, data mining, wireless networking, image processing, and fault diagnosis. He acts as an associate editor for two international journals including Multidimensional Systems and Signal Processing and International Journal of Pattern Recognition and Artificial Intelligence. 7. Use Git or checkout with SVN using the web URL. In 2006, Hinton et al. J. Yamanaka, S. Kuwashima, T. Kurita. Learning a single convolutional super-resolution network for multiple degradations. This article aims to provide a comprehensive sur T. Tong, G. Li, X. J. Liu, Q. Q. Gao. MathSciNet IEEE Transactions on Image Processing, vol. DOI: https://doi.org/10.1109/CVPR.2018.00329. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. Z. Hui, X. M. Wang, X. DOI: https://doi.org/10.1109/CVPR.2015.7299003. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. Currently, he is with Centre for Signal and Image Processing (CeSIP), University of Strathclyde, UK. International Journal of Automation and Computing Ha, V.K., Ren, JC., Xu, XY. Following postdoctoral and academic positions at the Universities of West of Scotland (19961998), Dundee (19982000) and Stirling (2000-2018), respectively, he joined Edinburgh Napier University (UK) in 2018, as founding director of the Cognitive Big Data and Cybersecurity (CogBiD) Research Laboratory, managing over 25 academic and research staffs. J. IEEE Transactions on Pattern Analysis and Machine Intelligence 2016 We propose a deep learning method for single image super-resolution (SR). A novel single-image super-resolution method is presented by introducing dense skip connections in a very deep network, providing an effective way to combine the low-level features and high- level features to boost the reconstruction performance. 3.1. This survey presents a deep review of the literature designed for students and professionals who want to know more about the topic. Interpolation-based upsampling methods. We are updating the information and adjusting the pages on this code! Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. A statistical prediction model based on sparse representations for single image super-resolution. DOI: https://doi.org/10.1007/s11633-010-0009-7. In Proceedings of British Machine Vision Conference, BMVA Press, Surrey, UK, 2012. 105114, 2017. Wide Activation for Efficient and Accurate Image Super-resolution, [Online], Available: https://arxiv.org/abs/1808.08718v1, April 8, 2019. T. Peleg, M. Elad. 439455, 2018. On single image scale-up using sparse-representations. He has published over 150 peer reviewed journals and conferences papers. A cascaded convolution neural network for image super-resolution (CSRCNN), which includes three cascaded Fast SRCNNs and each Fast S RCNN can process a specific scale image. DOI: https://doi.org/10.1109/CVPR.2015.7299156. 23, no. This survey is intended as a timely update and overview of deep learning approaches to image restoration and is organised as follows. Viet Khanh Ha received the B. Eng. D. degrees from the Taiyuan University of Technology, China, in 2002 and 2009, respectively. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). This paper develops a basic network learning external prior from large scale training data and then learns the internal prior from the given low-resolution image for task adaptation, and achieves 0.18 dB PSNR improvements over the basic networks results on standard datasets. His research interests include developing cognitive data science and AI technologies, to engineer the smart and secure systems of tomorrow. In Proceedings of IEEE International Conference on Computer Vision, Sydney, Australia, pp. There was a problem preparing your codespace, please try again. 38, no. Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that aims to obtain a high-resolution output from one of its low-resolution versions. A statistical evaluation of recent full reference image quality assessment algorithms, Mean squared error: Love it or leave it? Conf. In this survey, we aim to give a survey on recent advances of image super-resolution techniques using deep learning approaches in a systematic . 106119, 2018. Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. 2, pp. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Venice, Italy, pp. 3. Shi B., Zheng Y., Self-similarity constrained sparse representation for hyperspectral image super-resolution, IEEE Trans. This work proposes a new direction for fast video super-resolution via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution, and combines SR drafts through the nonlinear process in a deep convolutional neural network (CNN). A mask is introduced to separate the image into low- and high-frequency parts based on image gradient magnitude, and then a gradient sensitive loss is devised to well capture the structures in the image without sacrificing the recovery of low-frequency content. Enhanced deep residual networks for single image super-resolution. The basic image super-resolution methods based on deep learning have been discussed in detail along with the latest applications using super- resolution techniques, and the main application areas of image superresolution based onDeep learning domain are presented.
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