Fourier ring correlation simplifies image restoration in fluorescence microscopy

Abstract

Fourier ring correlation (FRC) has recently gained popularity among fluorescence microscopists as a straightforward and objective method to measure the effective image resolution. While the knowledge of the numeric resolution value is helpful in e.g., interpreting imaging results, much more practical use can be made of FRC analysis—in this article we propose blind image restoration methods enabled by it. We apply FRC to perform image de-noising by frequency domain filtering. We propose novel blind linear and non-linear image deconvolution methods that use FRC to estimate the effective point-spread-function, directly from the images. We show how FRC can be used as a powerful metric to observe the progress of iterative deconvolution. We also address two important limitations in FRC that may be of more general interest: how to make FRC work with single images (within certain practical limits) and with three-dimensional images with highly anisotropic resolution.

Genetically encoded sensors enable micro- and nano-scopic decoding of transmission in healthy and diseased brains

“…The rapidly increased applications of deconvolution microscopy attribute mainly to new genetic tools that yield more collectable photons and expeditiously improved computational algorithms [ 53 , 55 , 56 ]. Given that high-performance genetically encoded neurotransmitter sensors can achieve dense expression of fluorophores and emit large amounts of photons upon transmitter binding, it is possible to achieve wide-field living imaging of transmitter release at the ~100−200-nm resolution and resolve many fundamental synaptic properties of neurotransmission [ 32 ].…”

Optical nanoscopy

“…The inversion of the resulting value produces the value of the effective spatial resolution attainable by the image formation system being used, including all the actors, from optics to sample characteristics. In order to obtain two identical images under the condition of independent noise realizations, some practical approaches can be carried out, namely [60]: (a) frame-based acquisition: two consecutive measurements are performed, and a drift-correction applied; (b) line-based acquisition: every line is raster-scanned twice, and the two different images are formed by considering even and odd lines; (c) pixel-based acquisition: the pixel dwell-time of every pixel is split into two temporal windows having the same duration to obtain two independent images at the end of a single scan. It is worth noting that in the case of time-dependent phenomena, such as diffusion, it is possible to isolate "fake" spatial frequencies.…”

X-ray ptychographic mode of self-assembled CdSe/CdS octapod-shaped nanocrystals in thick polymers

“…between 2.5 and 3 times the Á image-pixel of the phased image, close to the estimated value of = 27 nm (De . In fact, the spatial resolution of any microscopy image is actually limited by an effective point spread function (PSF) that very often can be well approximated by a Gaussian function and, usually, the spatial resolution is interrelated with the PSF full width at half-maximum (FWHM) (Koho et al, 2019). In our phased maps one can put the standard deviations of the Gaussian SF equal to Á image-pixel .…”

“…We used this approach to restore averaged ptychographical phased maps obtained for the same sample region. Very recently, with a similar approach of combining deblurring and denoising stages, spatial resolution improvements on single fluorescence microscopy images have also been shown (Koho et al, 2019). Our approach is based on the classical total variation approach but with the addition of a bilateral filter regularization (Laghrib et al, 2015).…”