This paper reviews the third biennial challenge on spectral reconstruction from RGB images,
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence.
Through research on brain science, the intelligence of remote sensing algorithms can be …

In this paper, we summarize the 2nd NTIRE challenge on stereo image super-resolution
(SR) with a focus on new solutions and results. The task of the challenge is to super-resolve …

In recent years, there has been a growing interest in combining learnable modules with
numerical optimization to solve low-level vision tasks. However, most existing approaches …

As a hot research topic in remote sensing, effectively integrating the advantageous features
of multispectral and panchromatic images is the main challenge for fusing these two remote …

In this article, we propose an unsupervised deep learning method for positron emission
tomography (PET) reconstruction from incomplete data. This method utilizes the so-called …

Deraining is a significant and fundamental computer vision task, aiming to remove the rain
streaks and accumulations in an image or video. Existing deraining methods usually make …

Supervised deep learning is widely used in the field of rain and haze weather removal
currently and has achieved excellent results. However, it is challenging to learn the potential …

In recent years, building deep learning models from optimization perspectives has becoming
a promising direction for solving low-level vision problems. The main idea of most existing …