2021
DOI: 10.1364/prj.416437
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Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images

Abstract: Super-resolution structured illumination microscopy (SR-SIM) provides an up to two-fold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data, e.g. as a result of low excitation power or low exposure times, result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high quality reconstructed images. A r… Show more

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Cited by 67 publications
(21 citation statements)
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“…However, care should be taken to distinguish between approaches that try to extract information from an image and those that try to add information to an image. With good training, it has been possible to develop multiple deep-learning approaches that convert an image, or set of images (such as an SIM acquisition sequence), into a higher resolution image [84][85][86][87]. While processing a set of images may fall into the 'extract' category, processing a single image in such a way must necessarily fall into the 'add' category.…”
Section: Should Image Scanning Microscopy Be Considered a Form Of Sim...mentioning
confidence: 99%
“…However, care should be taken to distinguish between approaches that try to extract information from an image and those that try to add information to an image. With good training, it has been possible to develop multiple deep-learning approaches that convert an image, or set of images (such as an SIM acquisition sequence), into a higher resolution image [84][85][86][87]. While processing a set of images may fall into the 'extract' category, processing a single image in such a way must necessarily fall into the 'add' category.…”
Section: Should Image Scanning Microscopy Be Considered a Form Of Sim...mentioning
confidence: 99%
“…However, care should be taken to distinguish between approaches that try to extract information from an image and those that try to add information to an image. With good training, it has been possible to develop multiple deep-learning approaches that convert an image, or set of images (such as a SIM acquisition sequence), into a higher resolution image [67][68][69][70]. While processing a set of images may fall into the 'extract' category, processing a single image in such a way must necessarily fall into the 'add' category.…”
Section: Can We Generate 'True' Super-resolution Images From Simple I...mentioning
confidence: 99%
“…Recently, deep learning techniques have been widely used in several research fields, such as photonics research [ 9 , 10 , 11 ], biological imaging [ 12 , 13 ], material science [ 14 ], and image super-resolution [ 15 , 16 ], all of which demonstrate the advantages of these techniques. Similarly, the strategy based on deep learning has achieved excellent performance in a series of biometric technology problems, such as emotion recognition [ 17 , 18 ], gait recognition [ 19 ], fingerprint recognition [ 20 ], and voice signal recognition [ 21 ].…”
Section: Introductionmentioning
confidence: 99%