Results 71 to 80 of about 5,054,495 (195)

Burst Super-Resolution for Intraoperative Image Analysis Without High-Resolution Ground Truth

IEEE Access
Intraoperative imaging, despite advances in high-resolution sensors, often suffers from effective resolution loss due to motion blur, suboptimal illumination, acquisition distance, and real-time compression, reducing image clarity and limiting reliable ...
Ivan Rubin, Maxim Volynsky, Victor A. Kashchenko, Rifat Hamoudi, Svetlana Illarionova   +4 more
doaj   +1 more source

Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN

, 2023
One of the primary causes of blindness in the diabetic population is diabetic retinopathy (DR). Many people could have their sight saved if only DR were detected and treated in time.
Mamoona Humayun, Ghadah Alwakid, Walaa Gouda   +2 more
core   +1 more source

Sentinel-2 Super-Resolution with Real-ESRGAN using Satellite and Aerial Image Pairs and Color Correction Techniques

Improving the spatial resolution of satellite images offers considerable potential for a wide range of remote sensing applications. This study investigates the use of an Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) to improve ...
Kleebauer, Maximilian, Marz, Christopher, Horst, Daniel   +2 more
core   +1 more source

ESRGAN-DP: Enhanced super-resolution generative adversarial network with adaptive dual perceptual loss

, 2023
The proposal of perceptual loss solves the over-smoothing problem of images caused by pixel-wise loss and improves the visual quality of the images, but it also inevitably produces a large number of artifacts and distortions in images.
Wenqian Xu, Yuanyuan Liu, Bo Li, Jie Song, Huawei Yi, Xiaohui Li   +5 more
core   +1 more source

DPO-ESRGAN: Perceptually Enhanced Super-Resolution Using Direct Preference Optimization

Super-resolution (SR) is a long-standing task in the field of computer vision that aims to improve the quality and resolution of an image. ESRGAN is a representative generative adversarial network specialized to produce perceptually convincing SR images.
Hanhoon Park, Wonwoo Yun
core   +1 more source

Weed instance segmentation from UAV Orthomosaic Images based on Deep Learning

Smart Agricultural Technology
Weeds significantly impact agricultural production, and traditional weed control methods often harm soil health and environment. This study aimed to develop deep learning-based segmentation models in identifying weeds in potato fields captured by ...
Chenghao Lu, Klaus Gehring, Stefan Kopfinger, Heinz Bernhardt, Michael Beck, Simon Walther, Thomas Ebertseder, Mirjana Minceva, Yuncai Hu, Kang Yu   +9 more
doaj   +1 more source

Small Ship Detection Based on a Learning Model That Incorporates Spatial Attention Mechanism as a Loss Function in SU-ESRGAN

Ship monitoring using Synthetic Aperture Radar (SAR) data faces significant challenges in detecting small vessels due to low spatial resolution and speckle noise.
Yu Morita, Hiroshi Okumura, Kohei Arai
core   +1 more source

Optimization of Imaging Reconnaissance Systems Using Super-Resolution: Efficiency Analysis in Interference Conditions

Sensors
Image reconnaissance systems are critical in modern applications, where the ability to accurately detect and identify objects is crucial. However, distortions in real-world operational conditions, such as motion blur, noise, and compression artifacts ...
Marta Bistroń, Zbigniew Piotrowski
doaj   +1 more source

Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area

Scientific Reports
This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies.
Erzong Zheng, Yichen Zhang, Jiquan Zhang, Jiale Zhu, Jiahao Yan, Gexu Liu   +5 more
doaj   +1 more source

Single MR Image Super-Resolution using Generative Adversarial Network

, 2022
Spatial resolution of medical images can be improved using super-resolution methods. Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN) is one of the recent effective approaches utilized to produce higher resolution images, given
Rashid, Shawkh Ibne, Ebrahimi, Mehran, Shakibapour, Elham   +2 more
core