Multifocus image fusion is an impor

Multifocus image fusion is an important technique that aims to generate a single clean image by fusing multiple input images. In this paper, we propose a novel multilevel features convolutional neural network (MLFCNN) architecture for image fusion. In the MLFCNN model, all features learned from previous layers are passed to the subsequent layer. Inside every path between the previous layer and the subsequent layer, we add a 1 × 1 convolution module to reduce the redundancy. In our method, the source images first are fed to our pre-trained MLFCNN model to obtain the initial focus map. Then, the initial focus map is performed by morphological opening and closing operations and followed by a Gaussian filter to obtain the final decision map. Finally, the fused all-in-focus image is generated based on a weighted-sum strategy with the decision map. The experimental results demonstrate that the proposed method outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objectiveevaluations.

結果 (繁體中文) 1:

Multifocus image fusion is an important technique that aims to generate a single clean image by fusing multiple input images. In this paper, we propose a novel multilevel features convolutional neural network (MLFCNN) architecture for image fusion. In the MLFCNN model, all features learned from previous layers are passed to the subsequent layer. Inside every path between the previous layer and the subsequent layer, we add a 1 × 1 convolution module to reduce the redundancy. In our method, the source images first are fed to our pre-trained MLFCNN model to obtain the initial focus map. Then, the initial focus map is performed by morphological opening and closing operations and followed by a Gaussian filter to obtain the final decision map. Finally, the fused all-in-focus image is generated based on a weighted-sum strategy with the decision map. The experimental results demonstrate that the proposed method outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objective
評估。

結果 (繁體中文) 2:

多焦點圖像融合是一種重要的技術，旨在通過融合多個輸入圖像生成單個乾淨的圖像。本文提出了一種用於圖像融合的新型多級特徵卷積神經網路（MLFCNN）架構。在 MLFCNN 模型中，從以前的圖層中學到的所有要素都傳遞到後續圖層。在前一層和後續層之間的每個路徑中，我們添加一個 1 × 1 卷積模組以減少冗余。在我們的方法中，首先將源圖像輸入到我們預先訓練的 MLFCNN 模型中，以獲得初始焦點圖。然後，通過形態打開和關閉操作執行初始焦點圖，然後由高斯濾波器進行，以獲取最終決策圖。最後，根據與決策圖的加權和策略生成融合的一對焦圖像。實驗結果表明，該方法在定性和客觀上都優於一些最先進的圖像融合演算法。
評估。

結果 (繁體中文) 3:

多聚焦影像融合是一項重要的科技，其目的是通過融合多幅輸入影像來生成一幅清晰的影像。本文提出了一種用於影像融合的多層特徵卷積神經網路（MLFCNN）結構。在MLFCNN模型中，從上一層學習到的所有特徵都被傳遞到下一層。在前一層和後一層之間的每條路徑中，加入1×1卷積模塊，以减少冗餘。在我們的方法中，首先將源影像輸入到我們預先訓練好的MLFCNN模型中，以獲得初始聚焦圖。然後，對初始聚焦圖進行形態學開閉運算，然後用高斯濾波得到最終的判决圖。最後，利用決策圖的加權和策略生成融合後的全焦點影像。實驗結果表明，該方法在定性和客觀兩個方面都優於一些最新的影像融合算灋
評估。