Image super-resolution with an enhanced group convolutional neural network
CNNs with strong learning abilities are widely chosen to resolve super-resolution problem.
However, CNNs depend on deeper network architectures to improve performance of image …
However, CNNs depend on deeper network architectures to improve performance of image …
BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability
Due to the cross-domain distribution shift aroused from diverse medical imaging systems,
many deep learning segmentation methods fail to perform well on unseen data, which limits …
many deep learning segmentation methods fail to perform well on unseen data, which limits …
[HTML][HTML] A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method
The objective of image super-resolution is to reconstruct a high-resolution (HR) image with
the prior knowledge from one or several low-resolution (LR) images. However, in the real …
the prior knowledge from one or several low-resolution (LR) images. However, in the real …
A Generalized Tensor Formulation for Hyperspectral Image Super-Resolution Under General Spatial Blurring
Hyperspectral super-resolution is commonly accomplished by the fusing of a hyperspectral
imaging of low spatial resolution with a multispectral image of high spatial resolution, and …
imaging of low spatial resolution with a multispectral image of high spatial resolution, and …
Variational Bayes image restoration with compressive autoencoders
Regularization of inverse problems is of paramount importance in computational imaging.
The ability of neural networks to learn efficient image representations has been recently …
The ability of neural networks to learn efficient image representations has been recently …
A general variation-driven network for medical image synthesis
The significance of medical image synthesis has exponentially grown due to constrained
medical resources, making it a critical component in numerous clinical applications. This …
medical resources, making it a critical component in numerous clinical applications. This …
Enhancing Face Image Quality: Strategic Patch Selection with Deep Reinforcement Learning and Super-Resolution Boost via RRDB
E Altinkaya, B Barakli - IEEE Access, 2024 - ieeexplore.ieee.org
Facial super-resolution (FSR) is a critical research area whose goal is to improve visual
quality by converting low-resolution facial images to high resolution ones. Research in FSR …
quality by converting low-resolution facial images to high resolution ones. Research in FSR …
[HTML][HTML] Unified Interpretable Deep Network for Joint Super-Resolution and Pansharpening
Joint super-resolution and pansharpening (JSP) brings new insight into the spatial
improvement of multispectral images. How to efficiently balance the spatial and spectral …
improvement of multispectral images. How to efficiently balance the spatial and spectral …
InDeed: Interpretable image deep decomposition with guaranteed generalizability
Image decomposition aims to analyze an image into elementary components, which is
essential for numerous downstream tasks and also by nature provides certain interpretability …
essential for numerous downstream tasks and also by nature provides certain interpretability …
Blind super-resolution model based on unsupervised degenerate indication learning
Blind super-resolution of images based on contrast learning has achieved better
performance, which can distinguish between different degraded information. However, there …
performance, which can distinguish between different degraded information. However, there …