Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

We review solutions to the problem of depth estimation, arguably the most important subtask
in scene understanding. We focus on the single image depth estimation problem. Due to its …

In this paper, we propose a general image fusion framework based on the convolutional
neural network, named as IFCNN. Inspired by the transform-domain image fusion …

Infrared and visible image fusion technologies make full use of different image features
obtained by different sensors, retain complementary information of the source images during …

View-synthesis-based methods have shown very promising results for the task of
unsupervised depth estimation in single images. Most existing approaches synthesize a …

Recent studies have shown that joint depth and pose estimation using convolutional neural
networks (CNNs) can learn unlabelled monocular frames. However, three problems remain …

Background and objective Due to the depth of focus (DOF) limitations of the optical systems
of microscopes, it is often difficult to achieve full clarity from microscopic biomedical images …

For a monocular 360° image, depth estimation is a challenging because the distortion
increases along the latitude. To perceive the distortion, existing methods devote to …

Insect pest recognition has always been a significant branch of agriculture and ecology. The
slight variance among different kinds of insects in appearance makes it hard for human …

In this paper, we propose an iterative feature matching framework for self-supervised depth
estimation in indoor scenes. Conventional methods usually leverage the structure-from …