Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image
processing and computer vision, which can integrate images with multiple exposure levels …

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

An image quality matrix provides a significant principle for objectively observing an image
based on an alteration between the original and distorted images. During the past two …

This study proposes a novel unified and unsupervised end-to-end image fusion network,
termed as U2Fusion, which is capable of solving different fusion problems, including multi …

Poor weather conditions, such as fog, haze, and mist, cause visibility degradation in
captured images. Existing imaging devices lack the ability to effectively and efficiently …

Existing image fusion methods always use hand-crafted fusion rules due to the
uninterpretability of deep feature maps, which restrict the performance of networks and result …

In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion
framework that uses self-supervised multi-task learning. The framework is based on an …

Multi-modality image fusion provides more comprehensive and sophisticated information in
modern medical diagnosis, remote sensing, video surveillance, and so on. This paper …

Deep learning networks have recently demonstrated yielded impressive progress for multi-
exposure image fusion. However, how to restore realistic texture details while correcting …

Shadow removal is still a challenging task due to its inherent background-dependent and
spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful …