Deep learning-based single-image super-resolution: A comprehensive review
High-fidelity information, such as 4K quality videos and photographs, is increasing as high-
speed internet access becomes more widespread and less expensive. Even though camera …
speed internet access becomes more widespread and less expensive. Even though camera …
Robust fall detection in video surveillance based on weakly supervised learning
Fall event detection has been a research hotspot in recent years in the fields of medicine
and health. Currently, vision-based fall detection methods have been considered the most …
and health. Currently, vision-based fall detection methods have been considered the most …
Self-supervised cycle-consistent learning for scale-arbitrary real-world single image super-resolution
Whether conventional machine learning-based or current deep neural networks-based
single image super-resolution (SISR) methods, they are generally trained and validated on …
single image super-resolution (SISR) methods, they are generally trained and validated on …
Tccl-net: Transformer-convolution collaborative learning network for omnidirectional image super-resolution
X Chai, F Shao, Q Jiang, H Ying - Knowledge-Based Systems, 2023 - Elsevier
As virtual reality and metaverse become more and more popular, the Omnidirectional Image
(OI) has attracted extreme attention due to its immersive display characteristics. However …
(OI) has attracted extreme attention due to its immersive display characteristics. However …
Conditional stochastic normalizing flows for blind super-resolution of remote sensing images
Remote sensing images (RSIs) in real scenes may be disturbed by multiple factors, such as
optical blur, undersampling, and additional noise, resulting in complex and diverse …
optical blur, undersampling, and additional noise, resulting in complex and diverse …
Activating more information in arbitrary-scale image super-resolution
Single-image super-resolution (SISR) has experienced vigorous growth with the rapid
development of deep learning. However, handling arbitrary scales (eg, integers, non …
development of deep learning. However, handling arbitrary scales (eg, integers, non …
Geometry-assisted multi-representation view reconstruction network for light field image angular super-resolution
Light Field (LF) imaging enables many attractive applications since scene angular and
spatial information can be captured simultaneously. However, the limited angular resolution …
spatial information can be captured simultaneously. However, the limited angular resolution …
Efficient blind super-resolution imaging via adaptive degradation-aware estimation
Single image super-resolution (SISR) has achieved prominent success based on deep
learning. However, most SISR methods based on the specific degradation pattern, eg …
learning. However, most SISR methods based on the specific degradation pattern, eg …
Generation Diffusion Degradation: Simple and Efficient Design for Blind Super-Resolution
In the domain of computer vision, blind super-resolution is a key area focused on generating
high-resolution images with enhanced visual quality from low-resolution counterparts …
high-resolution images with enhanced visual quality from low-resolution counterparts …
Localization and saturation of degradation space for weakly-supervised real-world super-resolution
G Tang, H Ge, Y Liu, C Wu - Knowledge-Based Systems, 2024 - Elsevier
Degradation in the real world is highly complex and random, making real super-resolution
datasets expensive. A series of unsupervised methods attempt to model real degradations to …
datasets expensive. A series of unsupervised methods attempt to model real degradations to …