论文标题
基于梯度变换的多聚焦图像融合
Multi-Focus Image Fusion based on Gradient Transform
论文作者
论文摘要
多聚焦图像融合是一个充满挑战的研究领域，旨在通过整合集中专注和非关注的像素来提供完全集中的图像。大多数现有方法都遭受转移差异，不易登记的图像和数据依赖性的障碍。在这项研究中，我们介绍了一种基于梯度信息的新型多聚焦图像融合方法，该方法适合上述问题。所提出的方法首先使用Halftoning Inverse Halftoning（H-IH）变换从原始图像中生成渐变图像。然后，将梯度（EOG）和标准偏差函数的能量用作对梯度图像的焦点测量，以形成融合图像。最后，为了增强融合图像，使用多数投票方法应用了决策融合方法。在视觉和客观上将提出的方法与17种不同的新颖和常规技术进行比较。为了进行客观评估，使用6种不同的定量指标。据观察，根据视觉评估，所提出的方法有希望，并且根据客观评估中的六个指标中的五分之五，获得了83.3％的成功。
Multi-focus image fusion is a challenging field of study that aims to provide a completely focused image by integrating focused and un-focused pixels. Most existing methods suffer from shift variance, misregistered images, and data-dependent. In this study, we introduce a novel gradient information-based multi-focus image fusion method that is robust for the aforementioned problems. The proposed method first generates gradient images from original images by using Halftoning-Inverse Halftoning (H-IH) transform. Then, Energy of Gradient (EOG) and Standard Deviation functions are used as the focus measurement on the gradient images to form a fused image. Finally, in order to enhance the fused image a decision fusion approach is applied with the majority voting method. The proposed method is compared with 17 different novel and conventional techniques both visually and objectively. For objective evaluation, 6 different quantitative metrics are used. It is observed that the proposed method is promising according to visual evaluation and 83.3% success is achieved by being first in five out of six metrics according to objective evaluation.
