On the public citation graph

Enhancing Optical Remote Sensing Images Via Random Degradation and Adaptive Fusion Super-Resolution Networks

https://doi.org/10.2139/ssrn.5032995
0 signed · 43 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_18openalex-snapshot
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
inferredcites →https://doi.org/10.1007/978-3-030-11021-5_5openalex-snapshot
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
inferredcites →https://doi.org/10.1007/978-3-031-19797-0_33openalex-snapshot
Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
inferredcites →https://doi.org/10.1016/j.future.2020.11.001openalex-snapshot
Multiple improved residual networks for medical image super-resolution
inferredcites →https://doi.org/10.1016/j.inffus.2023.03.021openalex-snapshot
From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution
inferredcites →https://doi.org/10.1016/j.media.2020.101938openalex-snapshot
Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization
inferredcites →https://doi.org/10.1109/anti-cybercrime.2015.7351950openalex-snapshot
A novel multi-frame super resolution algorithm for surveillance camera image reconstruction
inferredcites →https://doi.org/10.1109/cvpr.2008.4587647openalex-snapshot
Image super-resolution as sparse representation of raw image patches
inferredcites →https://doi.org/10.1109/cvpr.2016.182openalex-snapshot
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.19openalex-snapshot
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
inferredcites →https://doi.org/10.1109/cvpr.2018.00068openalex-snapshot
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
inferredcites →https://doi.org/10.1109/cvpr.2018.00344openalex-snapshot
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
inferredcites →https://doi.org/10.1109/cvpr.2019.00175openalex-snapshot
Camera Lens Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00545openalex-snapshot
Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01044openalex-snapshot
Unsupervised Degradation Representation Learning for Blind Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01046openalex-snapshot
Flow-based Kernel Prior with Application to Blind Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01318openalex-snapshot
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00217openalex-snapshot
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01712openalex-snapshot
Deep Constrained Least Squares for Blind Image Super-Resolution
inferredcites →https://doi.org/10.1109/cvprw.2017.151openalex-snapshot
Enhanced Deep Residual Networks for Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00241openalex-snapshot
Real-World Super-Resolution via Kernel Estimation and Noise Injection
inferredcites →https://doi.org/10.1109/icccn.2007.4317975openalex-snapshot
A Fidelity Metric for Assessing Visual Quality of Color Images
inferredcites →https://doi.org/10.1109/iccv48922.2021.00475openalex-snapshot
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
inferredcites →https://doi.org/10.1109/iccv51070.2023.01136openalex-snapshot
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/iccvw54120.2021.00217openalex-snapshot
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
inferredcites →https://doi.org/10.1109/igarss.2019.8898875openalex-snapshot
Feature Sparsity in Convolutional Neural Networks for Scene Classification of Remote Sensing Image
inferredcites →https://doi.org/10.1109/jproc.2017.2675998openalex-snapshot
Remote Sensing Image Scene Classification: Benchmark and State of the Art
inferredcites →https://doi.org/10.1109/lgrs.2015.2439517openalex-snapshot
Fast Multiclass Vehicle Detection on Aerial Images
inferredcites →https://doi.org/10.1109/tassp.1981.1163711openalex-snapshot
Cubic convolution interpolation for digital image processing
inferredcites →https://doi.org/10.1109/tcsvt.2022.3188433openalex-snapshot
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/tgrs.2017.2685945openalex-snapshot
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
inferredcites →https://doi.org/10.1109/tgrs.2022.3180068openalex-snapshot
Multiattention Generative Adversarial Network for Remote Sensing Image Super-Resolution
inferredcites →https://doi.org/10.1109/tgrs.2023.3293912openalex-snapshot
UGIF-Net: An Efficient Fully Guided Information Flow Network for Underwater Image Enhancement
inferredcites →https://doi.org/10.1109/tgrs.2023.3341437openalex-snapshot
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
inferredcites →https://doi.org/10.1109/tip.2003.819861openalex-snapshot
Image quality assessment: from error visibility to structural similarity
inferredcites →https://doi.org/10.1109/tpami.2015.2439281openalex-snapshot
Image Super-Resolution Using Deep Convolutional Networks
inferredcites →https://doi.org/10.1117/1.jrs.10.035004openalex-snapshot
Feature significance-based multibag-of-visual-words model for remote sensing image scene classification
inferredcites →https://doi.org/10.14358/pers.85.4.297openalex-snapshot
Vehicle Detection in Aerial Images
inferredcites →https://doi.org/10.1609/aaai.v36i2.20030openalex-snapshot
Best-Buddy GANs for Highly Detailed Image Super-resolution
inferredcites →https://doi.org/10.3390/rs14122895openalex-snapshot
Single-Image Super Resolution of Remote Sensing Images with Real-World Degradation Modeling
inferredcites →https://doi.org/10.3390/rs15051391openalex-snapshot
SA-GAN: A Second Order Attention Generator Adversarial Network with Region Aware Strategy for Real Satellite Images Super Resolution Reconstruction
inferredcites →https://doi.org/10.3390/rs15205062openalex-snapshot
A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images
inferredcites →https://doi.org/10.48550/arxiv.1909.06581openalex-snapshot
Blind Super-Resolution Kernel Estimation using an Internal-GAN
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.