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Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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Absolute Motion Analysis- General Plane Motion

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Related Experiment Video

Updated: Jun 23, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Super resolution with probabilistic motion estimation.

Matan Protter, Michael Elad

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 19, 2009

Summary

This summary is machine-generated.

This study introduces a simpler super-resolution reconstruction (SRR) framework that bypasses explicit motion estimation. The new method uses probabilistic motion estimation for improved simplicity and offers extensions for tasks like de-interlacing.

Related Experiment Videos

Last Updated: Jun 23, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

Computer Vision
Image Processing

Background:

Traditional super-resolution reconstruction (SRR) heavily relies on precise inter-frame motion estimation.
Recent advancements explore SRR methods that circumvent explicit motion estimation.

Discussion:

This work presents a novel framework simplifying SRR by integrating probabilistic, crude motion estimation into the classic SRR formulation.
The approach enhances intuitiveness and accessibility compared to prior methods.

Key Insights:

The proposed framework achieves SRR without demanding explicit, high-accuracy motion estimation.
It offers a more straightforward and understandable methodology for SRR.

Outlook:

The framework's flexibility allows for extensions to diverse re-sampling tasks, including de-interlacing.
This opens avenues for broader applications in image and video enhancement.