scholarly journals Second-order optimized regularized structured illumination microscopy (sorSIM) for high-quality and rapid super resolution image reconstruction with low signal level

2020 ◽  
Vol 28 (11) ◽  
pp. 16708
Author(s):  
Wei Yu ◽  
Yangyang Li ◽  
Stijn Jooken ◽  
Olivier Deschaume ◽  
Fei Liu ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Second Order ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
High Quality ◽  
Resolution Image

scholarly journals Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images

2020 ◽  
Author(s):  
Zafran Hussain Shah ◽  
Marcel Müller ◽  
Tung-Cheng Wang ◽  
Philip Maurice Scheidig ◽  
Axel Schneider ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Super Resolution ◽  
Image Data ◽  
Excitation Power ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
High Quality ◽  
Reconstruction Methods ◽  
High Modulation

AbstractSuper-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 residual encoding-decoding convolution neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the entirely deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well over various noise levels. The combination of computational reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change.

scholarly journals Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction

GigaScience ◽  
2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Karl A Johnson ◽  
Guy M Hagen
Keyword(s):  
Super Resolution ◽  
Structured Illumination ◽  
Optical Sectioning ◽  
Structured Illumination Microscopy ◽  
High Quality ◽  
Related Data ◽  
Reconstruction Methods ◽  
Set Up ◽  
Bayesian Image Reconstruction ◽  
Hematoxylin Eosin

Abstract Background Structured illumination microscopy (SIM) is a method that can be used to image biological samples and can achieve both optical sectioning and super-resolution effects. Optimization of the imaging set-up and data-processing methods results in high-quality images without artifacts due to mosaicking or due to the use of SIM methods. Reconstruction methods based on Bayesian estimation can be used to produce images with a resolution beyond that dictated by the optical system. Findings Five complete datasets are presented including large panoramic SIM images of human tissues in pathophysiological conditions. Cancers of the prostate, skin, ovary, and breast, as well as tuberculosis of the lung, were imaged using SIM. The samples are available commercially and are standard histological preparations stained with hematoxylin-eosin. Conclusion The use of fluorescence microscopy is increasing in histopathology. There is a need for methods that reduce artifacts caused by the use of image-stitching methods or optical sectioning methods such as SIM. Stitched SIM images produce results that may be useful for intraoperative histology. Releasing high-quality, full-slide images and related data will aid researchers in furthering the field of fluorescent histopathology.

scholarly journals Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction

2019 ◽  
Author(s):  
Karl Johnson ◽  
Guy M. Hagen
Keyword(s):  
Super Resolution ◽  
Structured Illumination ◽  
Optical Sectioning ◽  
Structured Illumination Microscopy ◽  
High Quality ◽  
Related Data ◽  
Reconstruction Methods ◽  
Hematoxylin And Eosin ◽  
Bayesian Image Reconstruction ◽  
Data Processing Methods

AbstractBackgroundStructured illumination microscopy (SIM) is a method which can be used to image biological samples and can achieve both optical sectioning and super-resolution effects. Optimization of the imaging setup and data processing methods results in high quality images without artifacts due to mosaicking or due to the use of SIM methods. Reconstruction methods based on Bayesian estimation can be used to produce images with a resolution beyond that dictated by the optical system.FindingsFive complete datasets are presented including large panoramic SIM images of human tissues in pathophysiological conditions. Cancers of the prostate, skin, ovary, and breast, as well as tuberculosis of the lung, were imaged using SIM. The samples are available commercially and are standard histological preparations stained with hematoxylin and eosin.ConclusionThe use of fluorescence microscopy is increasing in histopathology. There is a need for methods which reduce artifacts when employing image stitching methods or optical sectioning methods such as SIM. Stitched SIM images produce results which may be useful for intraoperative histology. Releasing high quality, full slide images and related data will aid researchers in furthering the field of fluorescent histopathology.

scholarly journals Method of lateral image reconstruction in structured illumination microscopy with super resolution

2016 ◽  
Vol 09 (03) ◽  
pp. 1630002 ◽  
Author(s):  
Qiang Yang ◽  
Liangcai Cao ◽  
Hua Zhang ◽  
Hao Zhang ◽  
Guofan Jin
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Parameters Estimation ◽  
Gaussian Beams ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Reconstruction Process ◽  
Correlation Algorithm ◽  
Auto Correlation ◽  
Enhanced Resolution

The image reconstruction process in super-resolution structured illumination microscopy (SIM) is investigated. The structured pattern is generated by the interference of two Gaussian beams to encode undetectable spectra into detectable region of microscope. After parameters estimation of the structured pattern, the encoded spectra are computationally decoded and recombined in Fourier domain to equivalently increase the cut-off frequency of microscope, resulting in the extension of detectable spectra and a reconstructed image with about two-fold enhanced resolution. Three different methods to estimate the initial phase of structured pattern are compared, verifying the auto-correlation algorithm affords the fast, most precise and robust measurement. The artifacts sources and detailed reconstruction flowchart for both linear and nonlinear SIM are also presented.

scholarly journals Structured illumination microscopy based on asymmetric three-beam interference

2020 ◽  
pp. 2050027
Author(s):  
Linyu Xu ◽  
Yanwei Zhang ◽  
Song Lang ◽  
Hongwei Wang ◽  
Huijie Hu ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Super Resolution ◽  
Modulation Method ◽  
Imaging Method ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Light Modulator ◽  
Theoretical Derivation ◽  
Three Dimensional Imaging ◽  
Resolution Image

Structured illumination microscopy (SIM) is a rapidly developing super-resolution technology. It has been widely used in various application fields of biomedicine due to its excellent two- and three-dimensional imaging capabilities. Furthermore, faster three-dimensional imaging methods are required to help enable more research-oriented living cell imaging. In this paper, a fast and sensitive three-dimensional structured illumination microscopy based on asymmetric three-beam interference is proposed. An innovative time-series acquisition method is employed to halve the time required to obtain each raw image. A segmented half-wave plate as a substantial linear polarization modulation method is applied to the three-dimensional SIM system for the first time. Although it needs to acquire 21 raw images instead of 15 to reconstruct one super-resolution image, the SIM setup proposed in this paper is 30% faster than the traditional spatial light modulator-SIM (SLM-SIM) in imaging each super-resolution image. The related theoretical derivation, hardware system, and verification experiment are elaborated in this paper. The stable and fast 3D super-resolution imaging method proposed in this paper is of great significance to the research of organelle interaction, intercellular communication, and other biomedical fields.

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