Multi-focus image fusion is an effective technique to extend the depth-of-field of optical
lenses by creating an all-in-focus image from a set of partially focused images of the same …

By integrating the information contained in multiple images of the same scene into one
composite image, pixel-level image fusion is recognized as having high significance in a …

Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that
simultaneously contains functional metabolic information and structural tissue details …

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 …

Image fusion integrates a series of images acquired from different sensors, eg, infrared and
visible, outputting an image with richer information than either one. Traditional and recent …

In this paper, we present a novel deep learning architecture for infrared and visible images
fusion problems. In contrast to conventional convolutional networks, our encoding network is …

Existing image fusion approaches are committed to using a single deep network to solve
different image fusion problems, achieving promising performance in recent years. However …

Multi-focus image fusion (MFIF) is an important area in image processing. Since 2017, deep
learning has been introduced to the field of MFIF and various methods have been proposed …

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 …

The end-to-end image fusion framework has achieved promising performance, with
dedicated convolutional networks aggregating the multi-modal local appearance. However …