Mostafa Amin-Naji, A. Aghagolzadeh, Hami Mahdavinataj
{"title":"基于行列式的快速多焦点图像融合","authors":"Mostafa Amin-Naji, A. Aghagolzadeh, Hami Mahdavinataj","doi":"10.1109/MVIP53647.2022.9738555","DOIUrl":null,"url":null,"abstract":"This paper presents fast pixel-wise multi-focus image fusion in the spatial domain without bells and whistles. The proposed method just uses the determinant of the sliding windows from the input images as a metric to create a pixel-wise decision map. The sliding windows of 15 pixels with the stride of 7 pixels are passed through the input images. Then it creates a pixel-wise decision map for fusion multi-focus images. Also, some simple tricks like global image threshold using Otsu’s method and removal of small objects by morphological closing operation are used to refine the pixel-wise decision map. This method is high-speed and can fuse a pair of 512x512 multi-focus images around 0.05 seconds (50 milliseconds) in our hardware. We compared it with 22 prominent methods in the transform domain, spatial domain, and deep learning based methods that their source codes are available, and our method is faster than all of them. We conducted the objective and subjective experiments on the Lytro dataset, and our method can compete with their results. The proposed method may not have the best fusion quality among state-of-the-art methods, but to the best of our knowledge, this is the fastest pixel-wise method and very suitable for real-time image processing. All material and source code will be available in https://github.com/mostafaaminnaji/FastDetFuse and http://imagefusion.ir.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fast Multi Focus Image Fusion Using Determinant\",\"authors\":\"Mostafa Amin-Naji, A. Aghagolzadeh, Hami Mahdavinataj\",\"doi\":\"10.1109/MVIP53647.2022.9738555\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents fast pixel-wise multi-focus image fusion in the spatial domain without bells and whistles. The proposed method just uses the determinant of the sliding windows from the input images as a metric to create a pixel-wise decision map. The sliding windows of 15 pixels with the stride of 7 pixels are passed through the input images. Then it creates a pixel-wise decision map for fusion multi-focus images. Also, some simple tricks like global image threshold using Otsu’s method and removal of small objects by morphological closing operation are used to refine the pixel-wise decision map. This method is high-speed and can fuse a pair of 512x512 multi-focus images around 0.05 seconds (50 milliseconds) in our hardware. We compared it with 22 prominent methods in the transform domain, spatial domain, and deep learning based methods that their source codes are available, and our method is faster than all of them. We conducted the objective and subjective experiments on the Lytro dataset, and our method can compete with their results. The proposed method may not have the best fusion quality among state-of-the-art methods, but to the best of our knowledge, this is the fastest pixel-wise method and very suitable for real-time image processing. All material and source code will be available in https://github.com/mostafaaminnaji/FastDetFuse and http://imagefusion.ir.\",\"PeriodicalId\":184716,\"journal\":{\"name\":\"2022 International Conference on Machine Vision and Image Processing (MVIP)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Machine Vision and Image Processing (MVIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MVIP53647.2022.9738555\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Machine Vision and Image Processing (MVIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MVIP53647.2022.9738555","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents fast pixel-wise multi-focus image fusion in the spatial domain without bells and whistles. The proposed method just uses the determinant of the sliding windows from the input images as a metric to create a pixel-wise decision map. The sliding windows of 15 pixels with the stride of 7 pixels are passed through the input images. Then it creates a pixel-wise decision map for fusion multi-focus images. Also, some simple tricks like global image threshold using Otsu’s method and removal of small objects by morphological closing operation are used to refine the pixel-wise decision map. This method is high-speed and can fuse a pair of 512x512 multi-focus images around 0.05 seconds (50 milliseconds) in our hardware. We compared it with 22 prominent methods in the transform domain, spatial domain, and deep learning based methods that their source codes are available, and our method is faster than all of them. We conducted the objective and subjective experiments on the Lytro dataset, and our method can compete with their results. The proposed method may not have the best fusion quality among state-of-the-art methods, but to the best of our knowledge, this is the fastest pixel-wise method and very suitable for real-time image processing. All material and source code will be available in https://github.com/mostafaaminnaji/FastDetFuse and http://imagefusion.ir.