Deep learning in motion deblurring: current status, benchmarks and future prospects
Motion deblurring is one of the fundamental problems of computer vision and has received
continuous attention. The variability in blur, both within and across images, imposes …
continuous attention. The variability in blur, both within and across images, imposes …
Application of Deep Learning in Blind Motion Deblurring: Current Status and Future Prospects
Motion deblurring is one of the fundamental problems of computer vision and has received
continuous attention. The variability in blur, both within and across images, imposes …
continuous attention. The variability in blur, both within and across images, imposes …
Consolidating separate degradations model via weights fusion and distillation
Real-world images prevalently contain different varieties of degradation, such as motion blur
and luminance noise. Computer vision recognition models trained on clean images perform …
and luminance noise. Computer vision recognition models trained on clean images perform …
Three-dimensional integral imaging-based image descattering and recovery using physics informed unsupervised CycleGAN
Image restoration and denoising has been a challenging problem in optics and computer
vision. There has been active research in the optics and imaging communities to develop a …
vision. There has been active research in the optics and imaging communities to develop a …
Uncertainty-corrected fractional generalized Pareto motion for lithium-ion battery life prediction and value-at-risk-based maintenance framework
Z Wang, J Chen, Y Gao, W Song, HR Karimi… - Nonlinear …, 2025 - Springer
This paper presents a predictive methodology based on an uncertainty-corrected fractional
generalized Pareto motion (fGPM) to address challenges in self-capacity regeneration and …
generalized Pareto motion (fGPM) to address challenges in self-capacity regeneration and …
Adaptive control of nonlinear time-varying systems with unknown parameters and model uncertainties
Z Ma, Q Wang - Aerospace Science and Technology, 2024 - Elsevier
This paper investigates the adaptive control problem for nonlinear time-varying systems with
unknown parameters and model uncertainties. A novel class of switching functions is …
unknown parameters and model uncertainties. A novel class of switching functions is …
Underwater image restoration based on progressive guidance
Underwater images often suffer from local distortions during the imaging and transmission
process, which can negatively impact their quality. Fortunately, it is possible to improve …
process, which can negatively impact their quality. Fortunately, it is possible to improve …
[PDF][PDF] Image noise reduction by deep learning methods.
N Uzakkyzy, A Ismailova, T Ayazbaev… - International Journal of …, 2023 - academia.edu
Image noise reduction is an important task in the field of computer vision and image
processing. Traditional noise filtering methods may be limited by their ability to preserve …
processing. Traditional noise filtering methods may be limited by their ability to preserve …
Enhancing Image Quality by Reducing Compression Artifacts Using Dynamic Window Swin Transformer
Video/image compression codecs utilize the characteristics of the human visual system and
its varying sensitivity to certain frequencies, brightness, contrast, and colors to achieve high …
its varying sensitivity to certain frequencies, brightness, contrast, and colors to achieve high …
[HTML][HTML] Next-cell and mobility prediction in new generation cellular systems based on convolutional neural networks and encoding mobility data as images
Mobility prediction has been a popular research topic for many decades. With the advent of
new generation technologies (5G and beyond) and smaller coverage cells, hand-over …
new generation technologies (5G and beyond) and smaller coverage cells, hand-over …