In recent years, neural network-based methods have shown promising results in
hyperspectral image (HSI) denoising areas. Real HSIs exhibit the substantial variations in …

Limited by storage conditions, the degradation of old photos exhibits complex and diverse
features. Existing image restoration methods heavily rely on features extracted from a single …

In this paper, we propose a novel federated deep unrolling method for increasing the
accuracy of the Lidar Super resolution. The proposed framework not only offers notable …

Abstract Deep Equilibrium Models (DEQs) and Neural Ordinary Differential Equations
(Neural ODEs) are two branches of implicit models that have achieved remarkable success …

In this paper, the problem of lidar super-resolution is explored under a federated learning
perspective. The high cost of high-resolution lidar sensors is a major obstacle to the …

Considering the high cost of high-resolution Lidar sensors, in this work, a novel Lidar super-
resolution method is proposed to improve the performance on numerous autonomous …

To address the issues of inadequate feature expression capacity and poor adaptability of
feature fusion in traditional hyperspectral image classification methods, a new approach to …

This study aims to determine an acquisitional and computational workflow that yields the
highest quality spatio-spectral reconstructions in four-dimensional neutron tomography …

In this study the problem of Federated Learning (FL) is explored under a new perspective by
utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning …

Nowadays, various graph convolutional networks (GCNs) to process graph-structured data
have been proposed for hyperspectral image (HSI) classification. Nevertheless, most GCN …