Novel algorithm for multifocus image fusion: integration of convolutional neural network and partial differential equation
| Autor: | Gargi J Trivedi, Rajesh Sanghvi |
|---|---|
| Jazyk: | English<br />French |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Surveys in Mathematics and its Applications, Vol 19 (2024), Pp 179-195 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1843-7265 1842-6298 |
| Popis: | This paper presents a novel method for Multifocus image fusion that combines anisotropic diffusion PDE filtering and convolutional neural network (CNN) feature extraction. The proposed method aims to preserve image edges and details while reducing noise through the utilization of anisotropic diffusion PDE filtering. Additionally, a CNN architecture with ReLU activation function is employed for feature extraction. The method is evaluated on a dataset of Multifocus images and compared with traditional and CNN-based approaches, demonstrating superior performance in terms of visual quality and quantitative metrics, such as Normalized Mutual Information, Phase Congruency-based metric, and Structural Similarity-based metric. Furthermore, we aim to enhance our approach by incorporating machine learning techniques to optimize the parameters of the image fusion algorithm. By automatically adjusting these parameters, we strive to achieve the most reliable and accurate outcomes. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|