Results 11 to 20 of about 164,404 (265)

Super resolution for root imaging [PDF]

Applications in Plant Sciences, 2020
PremiseHigh‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection.
Jose F. Ruiz‐Munoz, Jyothier K. Nimmagadda, Tyler G. Dowd, James E. Baciak, Alina Zare   +4 more
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Super-resolution Ultrasound Imaging [PDF]

Ultrasound in Medicine & Biology, 2020
Ultrasound in medicine & biology 46(4), 865-891 (2020).
Christensen-Jeffries, Kirsten, Couture, Olivier, Dayton, Paul, Eldar, Yonina, Hynynen, Kullervo, Kiessling, Fabian, O'Reilly, Meaghan, Pinton, Gianmarco, Schmitz, Georg, Tang, Meng-Xing, Tanter, Mickaël, van Sloun, Ruud J.G.   +11 more
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HIGH RESOLUTION IMAGE CLASSIFICATION [PDF]

المجلة العراقية للعلوم الاحصائية, 2005
Classification is an important field with many applications. In particular, the classification of digital imagery has important applications in the mapping community.
Wasfi Taher Saalih
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Single‐image super‐resolution using lightweight transformer‐convolutional neural network hybrid model

IET Image Processing, 2023
With constant advances in deep learning methods as applied to image processing, deep convolutional neural networks (CNNs) have been widely explored in single‐image super‐resolution (SISR) problems and have attained significant success.
Yuanyuan Liu, Mengtao Yue, Han Yan, Lu Zhu   +3 more
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Enhanced Dense Space Attention Network for Super-Resolution Construction From Single Input Image

IEEE Access, 2021
In some applications, such as surveillance and biometrics, image enlargement is required to inspect small details on the image. One of the image enlargement approaches is by using convolutional neural network (CNN)-based super-resolution construction ...
Yoong Khang Ooi, Haidi Ibrahim, Muhammad Nasiruddin Mahyuddin   +2 more
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SUPER-RESOLUTION OF MULTISPECTRAL IMAGES [PDF]

Modelling and Simulation in Science, 2007
In this paper we propose and analyze a globally and locally adaptive super-resolution Bayesian methodology for pansharpening of multispectral images. The methodology incorporates prior knowledge on the expected characteristics of the multispectral images uses the sensor characteristics to model the observation process of both panchromatic and ...
Miguel Vega, Javier Mateos, Rafael Molina 0001, Aggelos K. Katsaggelos   +3 more
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Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images

Diagnostics, 2021
Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)-based AI systems to improve lesion detection and characterization in endoscopy.
Vajira Thambawita, Inga Strümke, Steven A. Hicks, Pål Halvorsen, Sravanthi Parasa, Michael A. Riegler   +5 more
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Reliable Perceptual Loss Computation for GAN-Based Super-Resolution With Edge Texture Metric

IEEE Access, 2021
Super-resolution (SR) is an ill-posed problem. Generating high-resolution (HR) images from low-resolution (LR) images remains a major challenge. Recently, SR methods based on deep convolutional neural networks (DCN) have been developed with impressive ...
J. Kim, C. Lee
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Matching images with different resolutions [PDF]

Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), 2002
In this paper we address the problem of matching two images with two different resolutions: a high-resolution image and a low-resolution one. On the premise that changes in resolution act as a smoothing equivalent to changes in scale, a scale-space representation of the high-resolution image is produced.
Dufournaud, Yves, Schmid, Cordelia, Horaud, Radu   +2 more
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Point cloud super‐resolution based on geometric constraints

IET Computer Vision, 2021
Among all digital representations we have for real physical objects, three‐dimensional (3D) is arguably the most expressive encoding. But due to the limitations of 3D scanning equipment, point cloud often becomes sparse or partially missing.
Xiaoqiang Li, Jitao Liu, Songmin Dai
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