# Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network

## TL;DR

This paper introduces a boundary aware deep neural network approach for multi-focus image fusion, significantly improving the quality of fused images especially near focus boundaries, outperforming existing methods.

## Contribution

A novel boundary aware deep neural network architecture with specialized handling of boundary regions and a new dataset generation method for multi-focus image fusion.

## Key findings

- Outperforms state-of-the-art methods quantitatively and qualitatively
- Effectively handles boundary regions near focus/defocus boundaries
- Uses a dual-network approach for different patch scenarios

## Abstract

Since it is usually difficult to capture an all-in-focus image of a 3D scene directly, various multi-focus image fusion methods are employed to generate it from several images focusing at different depths. However, the performance of existing methods is barely satisfactory and often degrades for areas near the focused/defocused boundary (FDB). In this paper, a boundary aware method using deep neural network is proposed to overcome this problem. (1) Aiming to acquire improved fusion images, a 2-channel deep network is proposed to better extract the relative defocus information of the two source images. (2) After analyzing the different situations for patches far away from and near the FDB, we use two networks to handle them respectively. (3) To simulate the reality more precisely, a new approach of dataset generation is designed. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods, both qualitatively and quantitatively.