High-order super-resolution optical fluctuation imaging based on low-pass denoising

Zou, Limin; Zhang, Su; Wang, Baokai; Tan, Jiubin

doi:10.1364/ol.43.000707

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…This issue is further complicated in a real experimental setting due to fluorescence bleaching, sample drift and similar factors. Various ways for noise filtering of the raw image stack have been proposed to decrease the frame number [133] or increase the cumulant order that can be computed for a given frame number [134], but have not been widely applied yet. Data pre-processing can also be used to correct for photobleaching artifacts and as a result improve the contrast of SOFI images [135].…”

Section: Choice Of Acquisition Parametersmentioning

confidence: 99%

Embracing the uncertainty: the evolution of SOFI into a diverse family of fluctuation-based super-resolution microscopy methods

et al. 2021

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Super-resolution microscopy techniques have pushed the limits of resolution in optical imaging by more than an order of magnitude. However, these methods often require long acquisition times as well as complex setups and sample preparation protocols. Super-resolution Optical Fluctuation Imaging (SOFI) emerged over ten years ago as an approach that exploits temporal and spatial correlations within the acquired images to obtain increased resolution with less strict requirements. This review follows the progress of SOFI from its ﬁrst demonstration to the development of a branch of methods that treat ﬂuctuations as a source of contrast, rather than noise. Among others, we highlight the implementation of SOFI with standard ﬂuorescent proteins as well as the microscope modiﬁcation that facilitate 3D imaging and the application of modern cameras. Going beyond the classical framework of SOFI, we explore diﬀerent innovative concepts from deep neural networks all the way to a quantum analogue of SOFI, antibunching microscopy. While SOFI has not reached the same level of ubiquity as other super-resolution methods, our overview ﬁnds signiﬁcant progress and substantial potential for the concept of leveraging ﬂuorescence ﬂuctuations to obtain super-resolved images.

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Section: Choice Of Acquisition Parametersmentioning

confidence: 99%

Embracing the uncertainty: the evolution of SOFI into a diverse family of fluctuation-based super-resolution microscopy methods

et al. 2021

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“…Besides, for weak sources and detectors with finite efficiency, the absolute values of cumulants rapidly decrease with the growth of their order n. Thus, the resolution enhancement can be also spoiled for a number of reasons mentioned in the Introduction, such as detectors imperfections and additional noise, emitters bleaching or other degradation of sources, image artefacts, etc. [6,10,16,17,[25][26][27][28][29][30][31][32].…”

Section: Basic Schemementioning

confidence: 99%

“…So, quite lot of research effort was spent in attempts to retrieve potentially infinite resolution enhancement predicted for SOFI by overcoming possible practical limitations in measuring high-order correlations, such as additional noise by imperfections of set-ups, finite size of detector pixels, emitters degradation, acquired image artefacts, etc. [6,10,16,17,[25][26][27][28][29][30][31][32]. Some effort was also spent on enhancing temporal resolution, i.e., on reducing the number of raw images [10,27,33,34].…”

Section: Introductionmentioning

confidence: 99%

Optimal correlation order in superresolution optical fluctuation microscopy

et al. 2020

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Here, we show that, contrary to the common opinion, the super-resolution optical fluctuation microscopy might not lead to ideally infinite super-resolution enhancement with increasing of the order of measured cumulants. Using information analysis for estimating error bounds on the determination of point sources positions, we show that reachable precision per measurement might be saturated with increasing of the order of the measured cumulants in the super-resolution regime. In fact, there is an optimal correlation order beyond which there is practically no improvement for objects of three and more point sources. However, for objects of just two sources, one still has an intuitively expected resolution increase with the cumulant order.

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“…Jiang et al proposed a modified SOFI algorithm to reduce the number of raw images from hundreds to tens by improving the fluctuating signal extraction process 9 . A new SOFI scheme was proposed recently to broaden its application for high‐order cumulants by suppressing shot noise using low‐pass denoising 26 . Watanabe et al proposed a SOFI variant of Variance imaging for super resolution (VISion), which calculates the multiple orders of variance to demonstrate a remarkable 90 nm and 80 ms spatiotemporal resolution by utilizing blinking enhanced QDs 27 .…”

Section: Introductionmentioning

confidence: 99%

Enhanced temporal and spatial resolution in super‐resolution covariance imaging algorithm with deconvolution optimization

Zhong

Wang

Xiong

et al. 2020

Journal of Biophotonics

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Based on the numerical analysis that covariance exhibits superior statistical precision than cumulant and variance, a new SOFI algorithm by calculating the n orders covariance for each pixel is presented with an almost 2n‐fold resolution improvement, which can be enhanced to 2n via deconvolution. An optimized deconvolution is also proposed by calculating the (n + 1) order SD associated with each n order covariance pixel, and introducing the results into the deconvolution as a damping factor to suppress noise generation. Moreover, a re‐deconvolution of the covariance image with the covariance‐equivalent point spread function is used to further increase the final resolution by above 2‐fold. Simulated and experimental results show that this algorithm can significantly increase the temporal–spatial resolution of SOFI, meanwhile, preserve the sample's structure. Thus, a resolution of 58 nm is achieved for 20 experimental images, and the corresponding acquisition time is 0.8 seconds.

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High-order super-resolution optical fluctuation imaging based on low-pass denoising

Cited by 10 publications

References 17 publications

Embracing the uncertainty: the evolution of SOFI into a diverse family of fluctuation-based super-resolution microscopy methods

Embracing the uncertainty: the evolution of SOFI into a diverse family of fluctuation-based super-resolution microscopy methods

Optimal correlation order in superresolution optical fluctuation microscopy

Enhanced temporal and spatial resolution in super‐resolution covariance imaging algorithm with deconvolution optimization

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