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Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs
[article]
2020
arXiv
pre-print
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To that end, we propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem, which brings an entirely novel perspective and solution to the problem compared ...
to existing methods that base their solution solely on producing a super-resolution version of the multispectral image. ...
This is achieved by designing a self-supervised learning procedure based on the colorization task rather than super-resolution task. ...
arXiv:2006.16644v1
fatcat:r56eynxgergkpex6p5ag2477qy
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., TIP 2020 6641-6654 Sensing Matrix Design for Compressive Spectral Imaging via Binary Principal Component Analysis. ...
Dudhane, A., +, TIP 2020 628-640 s-LWSR: Super Lightweight Super-Resolution Network. Li, B., +, TIP 2020 8368-8380 Self-Enhanced Convolutional Network for Facial Video Hallucination. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement
2019
Sensors
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In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution ...
facilitate subsequent analysis, such as classification and detection. ...
In this work, principal component analysis is initially applied to extract the major components from the highly correlated spectra, which are subsequently used for denoising the remaining components. ...
doi:10.3390/s19183929
fatcat:fp7lezjwcfg5fol5hxmgoejg7a
2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30
2020
IEEE transactions on circuits and systems for video technology (Print)
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., TCSVT Jan. 2020 217-231 Hu, X., see Zhu, L., TCSVT Oct. 2020 3358-3371 Hu, Y., Lu, M., Xie, C., and Lu, X ...
., and Zeng, B., MUcast: Linear Uncoded Multiuser TCSVT Nov. 2020 4299-4308 Hu, R., see Chen, L., TCSVT Dec. 2020 4513-4525 Hu, R., see Wang, X., TCSVT Nov. 2020 4309-4320 Hu, X., see Zhang, X ...
., +, TCSVT Feb. 2020 442-456
Image registration
RADAR: Robust Algorithm for Depth Image Super Resolution Based on
FRI Theory and Multimodal Dictionary Learning. ...
doi:10.1109/tcsvt.2020.3043861
fatcat:s6z4wzp45vfflphgfcxh6x7npu
Neural-networks model for force prediction in multi-principal-element alloys
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2101.05867v1 fatcat:dykljqw5y5gf5otdpe3lqalm3i
Atomistic simulations can provide useful insights into the physical properties of multi-principal-element alloys. ...
In order to provide insights into the effect of voxel resolution, we implemented two approaches based on the inner and outer bounding boxes. ...
These ideas were tested on a ternary TaNbMo MEA within a 54-atom random configurational model, created from Hybrid Cuckoo Search optimized Super-Cell Random Approximates (SCRAPs), 44 . ...
arXiv:2101.05867v2
fatcat:tolkf4h73zffjnhi5ij76a6ium
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. ...
., +, TIP 2021 7878-7888 Non-Greedy L21-Norm Maximization for Principal Component Analysis. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
Super-Resolution and Inpainting with Degraded and Upgraded Generative Adversarial Networks
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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Image super-resolution (SR) and image inpainting are two topical problems in medical image processing. ...
In addition, our method outperforms many existing super-resolution and inpainting approaches. ...
Dimension matching is therefore established by vectorizing and projecting the blur kernel onto an n-dimensional space via principal component analysis (PCA) and then stretching it into a real-valued tensor ...
doi:10.24963/ijcai.2020/90
dblp:conf/ijcai/HuangZWJW020
fatcat:pa7fpm35vjbnxa7sdxoqvg4dwi
Front Matter: Volume 10578
2018
Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
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, Cancer, Imaging Agents, and Bone and Musculoskeletal. ...
The diverse sessions included MRI and fMRI, Keynote and Emerging Trends, Neurological Imaging, Cardiovascular Imaging, Novel Imaging Techniques and Applications, Innovations in Image Processing, Optical ...
AND APPLICATIONS
10578 0L
Use of material decomposition in the context of neurovascular intervention using standard flat
panel and a high-resolution CMOS detector [10578-20]
10578 0M
Super-resolution ...
doi:10.1117/12.2323952
fatcat:om4wezsn3vgr7mebecadzuy5ly
Coherent, super resolved radar beamforming using self-supervised learning
[article]
2021
arXiv
pre-print
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R2-S2 is a family of algorithms which use a Deep Neural Network (DNN) with complex range-Doppler radar data as input and trained in a self-supervised method using a loss function which operates in multiple ...
Improvement of 4x in angular resolution was demonstrated using a real-world dataset collected in urban and highway environments during clear and rainy weather conditions. ...
Fig. 2 . 2 Radar super-resolution using self-supervised learning a-b. ...
arXiv:2106.13085v1
fatcat:oooeazjx65d3ha4wufef3kepju
Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future
2020
Applied Sciences
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The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches ...
well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. ...
, 2D wavelet transforms [82, 83] or two dimensional functional principal component analysis [84] . ...
doi:10.3390/app10207201
fatcat:tofpvyllzbautos4my26xajqfe
Brain Tumor Classification using Machine Learning
2021
Journal of Pharmaceutical Research International
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Using Principal Component Analysis on the data, miss-classification of tumor class in data is visualized. ...
This paper presents a technical analysis of tumor data with Machine Learning and Classification Approach. ...
For predictive modeling and exploratory data analysis Principal Component Analysis was used in this research. ...
doi:10.9734/jpri/2021/v33i59a34330
fatcat:ldktvn4wqjctjbvpgb4uha3o2a
A discriminative self‐attention cycle GAN for face super‐resolution and recognition
2021
IET Image Processing
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A discriminative self-attention cycle generative adversarial network is proposed for real-world face image super-resolution. ...
Most image superresolution methods adopt paired high-quality and its interpolated low-resolution version to train the super-resolution network. ...
and No. 61471013). ...
doi:10.1049/ipr2.12250
fatcat:f2aecfugkngrdcjddnccow7jpu
Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery
2020
Sensors
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This study uses the excellent "self-learning ability" of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition ...
High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. ...
(i) breeding, (j) unused land, (k) woodland and (C) Tongzhou New Town image fused by principal component substitution (PCS). ...
doi:10.3390/s20020397
pmid:31936791
pmcid:PMC7014233
fatcat:37n5fijasjfzzbzd5djspbdr34
Multimedia super-resolution via deep learning: A survey
2018
Digital signal processing (Print)
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2017 pre-print arXiv:1706.09077v1 fatcat:p6tzttwhrndwvebuandz3vfz4y
The recent phenomenal interest in convolutional neural networks (CNNs) must have made it inevitable for the super-resolution (SR) community to explore its potential. ...
In each case, first relevant benchmarks are introduced in the form of datasets and state of the art SR methods, excluding deep learning. ...
using random forests (RFs). ...
doi:10.1016/j.dsp.2018.07.005
fatcat:bhzritty4fcvhay4v2iptj5kge
A CNN-Based Pan-Sharpening Method for Integrating Panchromatic and Multispectral Images Using Landsat 8
2019
Remote Sensing
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panchromatic and low-spatial-resolution multispectral image. ...
To address the spectral and spatial distortions that adversely affect the conventional methods, a pan-sharpening method based on a convolutional neural network (CNN) architecture is proposed in this paper ...
For the methods based on component substitution, the insight behind them is the use of projection transformation (e.g., Gram-Schmidt [6] , intensity-hue-saturation (IHS) [7] , principal component analysis ...
doi:10.3390/rs11222606
fatcat:osujwptamzfl3azljmkgyreuzm
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