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 …

Coupled neural P (CNP) systems are recently developed distributed and parallel computing
models that are abstracted by the mechanisms of coupled and spiking neurons. CNP …

As an important carrier of data, images contain a huge amount of information. The purpose
of image fusion is to integrate the information from source images into a single image. Since …

Multi-focus image fusion is an important way to obtain all-in-focus images. The goal is to
reconstruct all the focused pixels in the source image into the fused image. Generally, in …

According to the imaging principle of the camera, the focusing and defocusing parts of the
image are often determined by the depth information in the real scene. Only objects within a …

Multi-focus image fusion aims to create an all-in-focus clear image by fusing a set of partially
focused images. In recent years, various multi-focus image fusion methods based on deep …

This paper proposes a method based on multi-dictionary linear sparse representation and
region fusion model for achieving high-quality multi-focus image fusion (MFIF). First, a multi …