Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images

The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope ac...

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Autores principales:	Jiyoung Lee, Seunghyun Jang, Jungbin Lee, Taehan Kim, Seonghan Kim, Jongbum Seo, Ki Hean Kim, Sejung Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	image fusion all-in-focus depth of field microscopy Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/427299bfb2774cb38893a22d023975fb
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spelling	oai:doaj.org-article:427299bfb2774cb38893a22d023975fb2021-11-11T19:18:31ZMulti-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images10.3390/s212173711424-8220https://doaj.org/article/427299bfb2774cb38893a22d023975fb2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7371https://doaj.org/toc/1424-8220The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: “full-reference” and “no-reference”. The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments.Jiyoung LeeSeunghyun JangJungbin LeeTaehan KimSeonghan KimJongbum SeoKi Hean KimSejung YangMDPI AGarticleimage fusionall-in-focusdepth of fieldmicroscopyChemical technologyTP1-1185ENSensors, Vol 21, Iss 7371, p 7371 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image fusion all-in-focus depth of field microscopy Chemical technology TP1-1185
spellingShingle	image fusion all-in-focus depth of field microscopy Chemical technology TP1-1185 Jiyoung Lee Seunghyun Jang Jungbin Lee Taehan Kim Seonghan Kim Jongbum Seo Ki Hean Kim Sejung Yang Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images
description	The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: “full-reference” and “no-reference”. The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments.
format	article
author	Jiyoung Lee Seunghyun Jang Jungbin Lee Taehan Kim Seonghan Kim Jongbum Seo Ki Hean Kim Sejung Yang
author_facet	Jiyoung Lee Seunghyun Jang Jungbin Lee Taehan Kim Seonghan Kim Jongbum Seo Ki Hean Kim Sejung Yang
author_sort	Jiyoung Lee
title	Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images
title_short	Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images
title_full	Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images
title_fullStr	Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images
title_full_unstemmed	Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images
title_sort	multi-focus image fusion using focal area extraction in a large quantity of microscopic images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/427299bfb2774cb38893a22d023975fb
work_keys_str_mv	AT jiyounglee multifocusimagefusionusingfocalareaextractioninalargequantityofmicroscopicimages AT seunghyunjang multifocusimagefusionusingfocalareaextractioninalargequantityofmicroscopicimages AT jungbinlee multifocusimagefusionusingfocalareaextractioninalargequantityofmicroscopicimages AT taehankim multifocusimagefusionusingfocalareaextractioninalargequantityofmicroscopicimages AT seonghankim multifocusimagefusionusingfocalareaextractioninalargequantityofmicroscopicimages AT jongbumseo multifocusimagefusionusingfocalareaextractioninalargequantityofmicroscopicimages AT kiheankim multifocusimagefusionusingfocalareaextractioninalargequantityofmicroscopicimages AT sejungyang multifocusimagefusionusingfocalareaextractioninalargequantityofmicroscopicimages
_version_	1718431579559165952

Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images

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