167 citations · 424 across the 41 of their papers we have counts for
9 papers · 1 filter
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework
Tingting Wu, Wenna Wu, Ying Yang +2
The Retinex model is one of the most representative and effective methods for low-light image enhancement. However, the Retinex model does not explicitly tackle the noise problem,…
Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior
Xiaole Tang, Xile Zhao, Jun Liu +3
Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-…
Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks
Jianfei Li, Chaoyan Huang, Raymond Chan +3
Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging. It i…
Snow Mask Guided Adaptive Residual Network for Image Snow Removal
Bodong Cheng, Juncheng Li, Ying Chen +2
Image restoration under severe weather is a challenging task. Most of the past works focused on removing rain and haze phenomena in images. However, snow is also an extremely commo…
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification
Cheng Chang, Tieyong Zeng
Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a data…
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Juncheng Li, Hanhui Yang, Qiaosi Yi +4
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of paramete…
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
Guangwei Gao, Zhengxue Wang, Juncheng Li +3
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning. However, these methods are difficult to be applied in real-world…
Convex Augmentation for Total Variation Based Phase Retrieval
Jianwei Niu, Hok Shing Wong, Tieyong Zeng
Phase retrieval is an important problem with significant physical and industrial applications. In this paper, we consider the case where the magnitude of the measurement of an unde…
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Guangwei Gao, Zixiang Xu, Juncheng Li +3
Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by jointly training with f…