Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network
This work addresses a specific issue in image processing for applications like photography or computer vision, but it is incremental as it builds on existing deep learning methods to improve boundary handling.
The paper tackles the problem of multi-focus image fusion, where existing methods degrade near focused/defocused boundaries, by proposing a boundary-aware deep neural network that uses two networks for different patch situations and a new dataset generation approach, resulting in outperforming state-of-the-art methods both qualitatively and quantitatively.
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.