Results 31 to 40 of about 3,028,783 (361)
IEEE Access, 2021 The purpose of single-image super resolution (SISR) is to reconstruct an accurate high-resolution image from a degraded low-resolution image. Owing to the lack of information in low-resolution images, SISR is a challenging problem.
Jiun Lee, Inyong Yun, Jaekwang Kim
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Residual Dense Network for Image Super-Resolution [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well.
Yulun Zhang +4 more
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Fluorescent sensors for imaging of interstitial calcium
Nature Communications, 2023 Calcium in interstitial fluids is central to systemic physiology and a crucial ion pool for entry into cells through numerous plasma membrane channels. Its study has been limited by the scarcity of methods that allow monitoring in tight inter-cell spaces
Ariel A. Valiente-Gabioud +8 more
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Cell Reports, 2023 Summary: Most gastrointestinal stromal tumors (GISTs) develop due to gain-of-function mutations in the tyrosine kinase gene, KIT. We recently showed that mutant KIT mislocalizes to the Golgi area and initiates uncontrolled signaling.
Yuuki Obata +8 more
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Image Super-Resolution Using Deep Convolutional Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images.
Chao Dong +3 more
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Designing a Practical Degradation Model for Deep Blind Image Super-Resolution [PDF]
IEEE International Conference on Computer Vision, 2021 It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.
K. Zhang +3 more
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Disentangling Light Fields for Super-Resolution and Disparity Estimation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Light field (LF) cameras record both intensity and directions of light rays, and encode 3D cues into 4D LF images. Recently, many convolutional neural networks (CNNs) have been proposed for various LF image processing tasks.
Yingqian Wang +6 more
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Multiple Frame Splicing and Degradation Learning for Hyperspectral Imagery Super-Resolution
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral imagery (HSI) is an emerging remote sensing technology to discriminate different remote sensing objects. However, the HSI spatial resolution is relatively low due to the trade-off in restricted physical hardware and various imaging ...
Chenwei Deng, Xingshi Luo, Wenzheng Wang
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IMPACT OF DEEP LEARNING-BASED SUPER-RESOLUTION ON BUILDING FOOTPRINT EXTRACTION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 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.
H. He +9 more
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Accurate Image Super-Resolution Using Very Deep Convolutional Networks [PDF]
Computer Vision and Pattern Recognition, 2015 We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19].
Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
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