Heterogeneous image fusion (HIF) techniques aim to enhance image quality by merging
complementary information from images captured by different sensors. Among these …

Camouflaged object detection (COD) is the challenging task of identifying camouflaged
objects visually blended into surroundings. Albeit achieving remarkable success, existing …

Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical
image into a high-quality (HQ) one without relying on paired images for training. While most …

The detection head constitutes a pivotal component within object detectors, tasked with
executing both classification and localization functions. Regrettably, the commonly used …

In recent years, deep-learning-based pixel-level unified image fusion methods have
received more and more attention due to their practicality and robustness. However, they …

Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of
degraded images and mitigate the adverse effects of deteriorated illumination. Among these …

In this paper, a unified optimization model for medical image fusion based on tensor
decomposition and the non-subsampled shearlet transform (NSST) is proposed. The model …

Infrared and visible image fusion (IVIF) aims to obtain an image that contains
complementary information about the source images. However, it is challenging to define …

Concealed object segmentation (COS) is a challenging task that involves localizing and
segmenting those concealed objects that are visually blended with their surrounding …

Learning-based Multi-View Stereo (MVS) methods aim to reconstruct 3D scenes from a set
of 2D calibrated images. However, existing learning-based MVS methods often overlook …