Results 11 to 20 of about 42,664 (162)
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Real-world degradations deviate from ideal degradations, as most deep learning-based scenarios involve the ideal synthesis of low-resolution (LR) counterpart images by popularly used bicubic interpolation. Moreover, supervised learning approaches rely on
Divya Mishra, Ofer Hadar
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GPSR: Gradient-Prior-Based Network for Image Super-Resolution
Applied Sciences, 2023 Recent deep learning has shown great potential in super-resolution (SR) tasks. However, most deep learning-based SR networks are optimized via pixel-level loss (i.e., L1, L2, and MSE), which forces the networks to output the average of all possible ...
Xiancheng Zhu +4 more
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BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK
Iraqi Journal of Information & Communication Technology, 2021 Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image.
Meryem H. Muhson, Ayad A. Al-Ani
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Deep Learning-Based Single-Image Super-Resolution: A Comprehensive Review
IEEE Access, 2023 High-fidelity information, such as 4K quality videos and photographs, is increasing as high-speed internet access becomes more widespread and less expensive.
Karansingh Chauhan +7 more
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IET Image Processing, 2021 The current super‐resolution (SR) deep network is mainly applied to the common image and pays little attention to the image with noise. The remote sensing image contains much noise, so that the SR reconstruction effect is not satisfactory.
Xin Yang +3 more
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Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy. [PDF]
, 2012 Lensfree in-line holographic microscopy offers sub-micron resolution over a large field-of-view (e.g., ~24 mm2) with a cost-effective and compact design suitable for field use. However, it is limited to relatively low-density samples.
Greenbaum, Alon, Ozcan, Aydogan
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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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A sub-Nyquist co-prime sampling music spectral approach for natural frequency identification of white-noise excited structures [PDF]
, 2018 Motivated by practical needs to reduce data transmission payloads in wireless sensors for vibration-based monitoring of civil engineering structures, this paper proposes a novel approach for identifying resonant frequencies of white-noise excited ...
Giaralis, A., Gkoktsi, K.
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Pixel-Level Kernel Estimation for Blind Super-Resolution
IEEE Access, 2021 Throughout the past several years, deep learning-based models have achieved success in super-resolution (SR). The majority of these works assume that low-resolution (LR) images are ‘uniformly’ degraded from their corresponding high ...
Jaihyun Lew, Euiyeon Kim, Jae-Pil Heo
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Computational structured illumination for high-content fluorescent and phase microscopy [PDF]
, 2019 High-content biological microscopy targets high-resolution imaging across large fields-of-view (FOVs). Recent works have demonstrated that computational imaging can provide efficient solutions for high-content microscopy.
Chowdhury, Shwetadwip +2 more
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