Results 41 to 50 of about 1,040 (193)

Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic Accuracy

IEEE Access
Medical Image Super-Resolution has always been a subject of interest in medical image processing. However, super-resolved retinal images are a requisite tool for doctors to properly diagnose and treat ophthalmic diseases.
K. Deepthi, Aditya K. Shastry
doaj   +1 more source

Deep learning‐based super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan

Remote Sensing in Ecology and Conservation, Volume 12, Issue 5, Page 712-726, October 2026.
This study presents a UAV‐based framework that integrates deep learning‐based super‐resolution reconstruction and an enhanced YOLO detector to improve centimetre‐scale benthic organism monitoring. Using hermit crabs in Lake Hamana, a coastal lagoon in Japan, as a case study, the method substantially enhanced small‐object detection performance ...
Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno   +10 more
wiley   +1 more source

Enhancing Post-Disaster Damage Detection and Recovery Monitoring by Addressing Class Imbalance in Satellite Imagery Using Enhanced Super-Resolution GANs (ESRGAN) [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Access to very high-resolution (HR) satellite imagery is often limited, delayed, or cost-prohibitive, restricting accurate and timely post-disaster damage detection and recovery monitoring (PDDRM). Additionally, class imbalance in disaster classification
U. Lagap, S. Ghaffarian
doaj   +1 more source

S³-ESRGAN: Enhanced Super-Resolution Generative Adversarial Network for Remote Sensing Imagery Spatial Resolution Improvement—An Application Using Sentinel-2 and UAV Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
This study introduces a deep learning-based super-resolution approach to enhance the spatial resolution of Sentinel-2 imagery using uncrewed aerial vehicle (UAV) data. The proposed scale-adaptive, spatial-attentive, and spectral-preserving enhanced super-
Ahmad Toosi, Farhad Samadzadegan, Farzaneh Dadrass Javan   +2 more
doaj   +1 more source

Super‐Resolution of Planetary Images Based on Generative Adversarial Network

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Currently, satellite imagery serves as the primary means of observing terrestrial planets such as the Mars, the Moon, and Mercury. Enhancing the resolution and quality of these images can provide more detailed insights into planetary surfaces. However, improvements in image quality are often limited by the constraints of sensor technology and ...
Xiaoran Zhang, Yiran Wang, Miao Zhuo
wiley   +1 more source

Garryanalyzer: A morphometric workflow and open‐source ImageJ plug‐in for quantitative morphological analysis of Pacific Northwest Quercus leaves

Applications in Plant Sciences, Volume 14, Issue 4, July-August 2026.
Abstract Premise Accurate species identification is crucial for ecological restoration and can be especially challenging for understudied non‐model species. Quercus garryana is the only native oak species in the Pacific Northwest and is an important component of the endangered oak savanna ecosystem.
Ziqi Xie, Tan Bahar, Keith Karoly
wiley   +1 more source

Underwater Image Super-Resolution using Generative Adversarial Network-based Model [PDF]

, 2023
Single image super-resolution (SISR) models are able to enhance the resolution and visual quality of underwater images and contribute to a better understanding of underwater environments. The integration of these models in Autonomous Underwater Vehicles (
Aghelan, Alireza, Rouhani, Modjtaba
core   +1 more source

A High-Quality Rice Leaf Disease Image Data Augmentation Method Based on a Dual GAN

IEEE Access, 2023
Deep learning models need sufficient training samples to support them in the training process; otherwise, overfitting occurs, resulting in model failure.
Zhao Zhang, Quan Gao, Lirong Liu, Yun He
doaj   +1 more source

Terrestrial Ecosystem Detection Based on Deep Learning Framework and Satellite Image Geographical Information

Geoscience Data Journal, Volume 13, Issue 3, July 2026.
The proposed deep learning framework integrates ResNet‐50 and LSTM models to detect and classify terrestrial ecosystems from satellite imagery. The workflow begins with image preprocessing using bilateral, guided, and median filters to enhance image quality and preserve edges.
Liang Dong, Fan Lei, Dongliang Huang, Yuye Gong, Jide Wei, Wenjing She   +5 more
wiley   +1 more source

A Mamba-Based Approach for Super-Resolution and Speckle Noise Reconstruction of SAR Images in Remote Sensing Applications

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Synthetic aperture radar (SAR) imagery is often degraded by multiplicative speckle and limited resolution, hindering reliable interpretation. To address this, we proposed MambaR-enhanced super-resolution generative adversarial networks (ESRGAN), a ...
Songhan Luan, Hongjie He, Liangzhi Li, Li Li, Nan Chen, Hongxin Yang, Jing Teng, Dedong Zhang, Jonathan Li   +8 more
doaj   +1 more source