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 …

The image fusion methods based on deep learning has become a research hotspot in the
field of computer vision in recent years. This paper reviews these methods from five aspects …

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 …

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 …

As is well known, activity level measurement and fusion rule are two crucial factors in image
fusion. For most existing fusion methods, either in spatial domain or in a transform domain …

We propose a straightforward and efficient fusion-based method for enhancing weakly
illumination images that uses several mature image processing techniques. First, we employ …

As a powerful assistance technique for biomedical diagnosis, multimodal medical image
fusion has emerged as a hot topic in recent years. Unfortunately, the trade-off among fusion …

Muti-focus image fusion is the extraction of focused regions from different images to create
one all-in-focus fused image. The key point is that only objects within the depth-of-field have …

Abstract The Convolutional Neural Networks (CNNs) based multi-focus image fusion
methods have recently attracted enormous attention. They greatly enhanced the constructed …

This paper proposes a pixel-wise convolutional neural network (p-CNN) that can recognize
the focused and defocused pixels in source images from its neighbourhood information for …