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Deep learning in pore scale imaging and modeling
Pore-scale imaging and modeling has advanced greatly through the integration of Deep
Learning into the workflow, from image processing to simulating physical processes. In …
Learning into the workflow, from image processing to simulating physical processes. In …
Ntire 2020 challenge on real-world image super-resolution: Methods and results
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on
the participating methods and final results. The challenge addresses the real world setting …
the participating methods and final results. The challenge addresses the real world setting …
Fourier space losses for efficient perceptual image super-resolution
Many super-resolution (SR) models are optimized for high performance only and therefore
lack efficiency due to large model complexity. As large models are often not practical in real …
lack efficiency due to large model complexity. As large models are often not practical in real …
Fine perceptive gans for brain mr image super-resolution in wavelet domain
Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration.
However, limited by factors such as imaging hardware, scanning time, and cost, it is …
However, limited by factors such as imaging hardware, scanning time, and cost, it is …
Ntire 2020 challenge on spectral reconstruction from an rgb image
This paper reviews the second challenge on spectral reconstruction from RGB images, ie,
the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image …
the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image …
Ntire 2020 challenge on nonhomogeneous dehazing
This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images
(restoration of rich details in hazy image). We focus on the proposed solutions and their …
(restoration of rich details in hazy image). We focus on the proposed solutions and their …
Land use land cover classification of remote sensing images based on the deep learning approaches: a statistical analysis and review
Over the last few years, deep learning (DL) techniques have gained popularity and have
become the new standard for data processing in remote sensing analysis. Deep learning …
become the new standard for data processing in remote sensing analysis. Deep learning …
Aim 2020 challenge on efficient super-resolution: Methods and results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with
focus on the proposed solutions and results. The challenge task was to super-resolve an …
focus on the proposed solutions and results. The challenge task was to super-resolve an …
Remote sensing data fusion with generative adversarial networks: State-of-the-art methods and future research directions
In the past decades, remote sensing (RS) data fusion has always been an active research
community. A large number of algorithms and models have been developed. Generative …
community. A large number of algorithms and models have been developed. Generative …
Perceptual extreme super-resolution network with receptive field block
T Shang, Q Dai, S Zhu, T Yang… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
Abstract Perceptual Extreme Super-Resolution for single image is extremely difficult,
because the texture details of different images vary greatly. To tackle this difficulty, we …
because the texture details of different images vary greatly. To tackle this difficulty, we …