Spectral super-resolution (sSR) is a very important technique to obtain hyperspectral images
from only RGB images, which can effectively overcome the high acquisition cost and low …

Rain can cause performance degradation of outdoor computer vision tasks. Thus, the
exploration of rain removal from videos or a single image has drawn considerable attention …

Low-light image enhancement plays very important roles in low-level vision areas. Recent
works have built a great deal of deep learning models to address this task. However, these …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Image reconstruction from undersampled k-space data has been playing an important role
in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated …

Real-world low-light images suffer from two main degradations, namely, inevitable noise
and poor visibility. Since the noise exhibits different levels, its estimation has been …

We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
In solving this problem, we depart from classical purely model-based approaches and …

Optimization theory assisted algorithms have received great attention for precoding design
in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant …

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as
imbalanced learning and recommender systems. The vast majority of existing AUC …

Low-light image enhancement aims to improve the quality of images captured under low-
lightening conditions, which is a fundamental problem in computer vision and multimedia …