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 images captured by the cameras contain distortions, misclassified pixels, uncertainties
and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input …

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 an important branch in the field of image fusion, the multi-focus image fusion technique
can effectively solve the problem of optical lens depth of field, making two or more partially …

Multi-focus image fusion methods can be mainly divided into two categories: transform
domain methods and spatial domain methods. Recent emerged deep learning (DL)-based …

The goal of multi-focus image fusion is to integrate all focus pixels from the source images
into the fused result and simultaneously avoid the introduction of defocused pixels …

Abstract Multi-Focus Image Fusion (MFIF) is a method that combines two or more source
images to obtain a single image which is focused, has improved quality and more …

Multi-focus image fusion technology is to extract different focused regions of the same scene
among partially focused images and merge them together to generate a composite image …

Traditional techniques based on image fusion are arduous in integrating complementary or
heterogeneous infrared (IR)/visible (VS) images. Dissimilarities in various kind of features in …

Multi-focus image fusion combines the focused parts of multiple images of the same scene
to generate a fully focused image. Dual Channel Pulse coupled neural network (DCPCNN) …