Sub-pixel Resolution
Sub-pixel Resolution - Related Videos
08:41
Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Cited by 11 •
2012
Lensfree optical tomography is a three-dimensional microscopy technique that offers a spatial resolution of <1 μm × <1 μm × <3 μm in x, y and z dimensions, respectively, over a large imaging-volume of 15-100 mm3, which can be particularly useful for integration with lab-on-a-chip platforms.
16:52
Test Samples for Optimizing STORM Super-Resolution Microscopy
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Cited by 46 •
2013
We describe the preparation of three test samples and how they can be used to optimize and assess the performance of STORM microscopes. Using these examples we show how to acquire raw data and then process it to acquire super-resolution images in cells of approximately 30-50 nm resolution.
08:47
Super-resolution Imaging of the Bacterial Division Machinery
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Cited by 17 •
2013
We describe a super-resolution imaging method to probe the structural organization of the bacterial FtsZ-ring, an essential apparatus for cell division. This method is based on quantitative analyses of photoactivated localization microscopy (PALM) images and can be applied to other bacterial cytoskeletal proteins.
07:55
Ground State Depletion Super-resolution Imaging in Mammalian Cells
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Cited by 16 •
2017
This article outlines a protocol for the detection of one or more plasma and/or intracellular membrane proteins using Ground State Depletion (GSD) super-resolution microscopy in mammalian cells. Here, we discuss the benefits and considerations of using such approaches for the visualization and quantification of cellular proteins.
02:35
High-Resolution Confocal Imaging of Pathogen-Infected Drosophila Hemocytes
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2026
Source: Hiroyasu, A., et al. Extraction of Hemocytes from Drosophila melanogaster Larvae for Microbial Infection and Analysis. J. Vis. Exp. (2018)This video demonstrates high-resolution confocal imaging of Drosophila melanogaster hemocytes infected with Coxiella burnetii to visualize pathogen localization in 3D, enabling detailed analysis of intracellular infection dynamics and host-pathogen interactions.
FAQs
Optical models interpret the pattern formed when light from a feature spreads across neighboring detector pixels. By relating measured signal intensities to the expected optical pattern, the analysis estimates the feature’s position within a pixel rather than assigning it only to the brightest pixel. This supports more precise localization of fluorescent labels, molecular movement, and other biological structures.
Sub-pixel resolution extracts positional information from the intensity distribution already recorded across multiple pixels. It therefore improves localization through analysis, interpolation, optical modeling, or repeated measurements without requiring a detector with physically smaller pixels. The approach is useful when the existing imaging system captures a measurable signal pattern but changing the detector is impractical or unnecessary.
Signal-to-noise ratio, optical calibration, sampling, and specimen stability directly influence the reliability of the estimate. Weak or noisy signals make intensity patterns harder to interpret, while calibration errors can bias the inferred position. Inadequate sampling and movement of the specimen can also reduce consistency, so sub-pixel results should be interpreted alongside the imaging conditions that produced them.
A typical workflow examines how a feature’s signal is distributed across adjacent pixels, applies an interpolation or optical-modeling procedure, and estimates the position or structure that best matches the measured pattern. Repeated measurements can further support the estimate. Appropriate optical calibration and sampling are important throughout the workflow because they determine whether the calculated position reflects the specimen accurately.
The approach can refine measurements of molecular movement, cell boundaries, organelle dynamics, and fluorescent labels. These applications depend on extracting positional or structural information from image intensity patterns rather than relying only on whole-pixel assignments. As a result, quantitative microscopy can track biological features more precisely while retaining the existing detector’s pixel arrangement.
Image registration requires accurate alignment of image features, and sub-pixel estimation can improve the calculated position of those features across images. In quantitative microscopy, the same capability helps measure changes in location, boundaries, or dynamics with greater precision. Its value depends on stable specimens, suitable sampling, adequate signal quality, and calibration that remains consistent across measurements.