Abstract
Deconvolution microscopy is a computational image-processing technique used in conjunction with fluorescence microscopy to increase the resolution and contrast of three-dimensional images. Fluorescence microscopy is a widely used technique in biology and medicine that involves labeling specific molecules or structures within a sample with fluorescent dyes and then electronically photographing the sample through a microscope. However, the resolution of conventional fluorescence microscopy is limited by diffraction within the microscope’s optical path, which causes blurring of the image and reduces the ability to resolve structures in close proximity with one another. Deconvolution microscopy overcomes this limitation by means of computer-based image processing whereby mathematical algorithms are used to eliminate the blurring caused by the microscope’s optics and thus obtain a higher-resolution image that reveals the fine details of the sample with greater accuracy. Deconvolution microscopy, which can be applied to a range of image acquisition modalities, including widefield, confocal, and super-resolution microscopy, has become an essential tool for studying the structure and function of biological systems at the cellular and molecular levels. In this perspective, the latest deconvolution techniques have been introduced and image-processing methods for medical purposes have been presented.
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