Monocular depth estimation has drawn widespread attention from the vision community due
to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep …

In recent years, deep learning super-resolution models for progressive reconstruction have
achieved great success. However, these models which refer to multi-resolution analysis …

Multi-exposure image fusion (MEF) is an affordable and convenient option for high-dynamic-
range imaging. Current MEF methods are prone to visually unrealistic results since they take …

Low-cost inertial measurement units (IMUs) suffer from low sensitivity and high random walk
noise, which makes it challenging to use them directly for dead reckoning. Regular model …

Purpose The paper aims to tackle the classification of Remote Sensing Images (RSIs), which
presents a significant challenge for computer algorithms due to the inherent characteristics …

Anomaly detection for orbiting satellites has become a paramount research focus in the
aerospace domain. Data-driven methodologies, employing high-dimensional telemetry data …

There are shadow and highlight regions in a low-dynamic-range (LDR) image which is
captured from a high-dynamic-range (HDR) scene. It is an ill-posed problem to restore the …

Due to saturated regions of low dynamic range (LDR) images and large intensity changes
among them, it is challenging to produce an information-enriched panoramic LDR image …

Recent years have seen increased interest in object detection‐based applications for fire
detection in digital images and videos from edge devices. The environment's complexity and …

Multi-exposure image fusion (MEF) technology is to generate a normally exposed image by
fusing images with different exposure levels. Most of the existing models use CNNs to …