Optical Artifact Reduction
Optical Artifact Reduction - Related Videos
03:11
Oxidation-Reduction Reactions
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2020
Oxidation–Reduction Reactions Earth’s atmosphere contains about 20% molecular oxygen, O2, a chemically reactive gas that plays an essential role in the metabolism of aerobic organisms and in many environmental processes that shape the world. The term oxidation was originally used to describe chemical reactions involving O2, but its meaning has evolved to refer to a broad and important reaction class known as oxidation–reduction (redox) reactions. Some redox reactions involve the transfer of...
08:31
Three-dimensional Optical-resolution Photoacoustic Microscopy
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Cited by 27 •
2011
Optical-resolution photoacoustic microscopy (OR-PAM) is an emerging technology capable of imaging optical absorption contrasts in vivo with cellular resolution and sensitivity. Here, we provide a visualized instruction on the experimental protocols of OR-PAM, including system configuration, system alignment, typical in vivo experimental procedures, and functional imaging schemes.
09:39
Optical Biosensing
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2023
Optical biosensors utilize light to detect the binding of a target molecule. These sensors can utilize a label molecule, which produces a measurable signal such as fluorescence, or these sensors can be label-free and use the changes in optical properties, such as refractive index, to sense for the binding of the target molecule. This video introduces both label and label-free optical biosensors, demonstrates their use in the laboratory, and shows some applications of the technology.
01:22
A Reduction of Uncertainty
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2026
Lease contracts are essential in reducing financial uncertainties that could impact a firm’s stability. One significant uncertainty is the residual value of an asset at the end of its lease term or useful life. The residual value represents the estimated worth of an asset upon disposal, which can fluctuate due to market conditions and technological changes.By assuming residual value risk, lessors leverage their asset valuation and resale expertise to manage depreciation and market fluctuations.
13:39
Optical Trapping of Nanoparticles
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Cited by 7 •
2013
The following setup approach details low power optical trapping of dielectric nanoparticles using a double-nanohole in metal film.
FAQs
Reliable correction begins by identifying where distortion enters the imaging chain. Lens aberration changes spatial representation, glare and scattering add unwanted optical signal, uneven illumination alters background intensity, and detector electronics contribute noise. Classifying the source guides the remedy: optical design can limit distortion, calibration can characterize system behavior, and filtering or reconstruction can address residual signals without treating genuine structure as an artifact.
Calibration establishes how an imaging system contributes to the recorded image before researchers interpret the scene or specimen. It supports correction of detector-related effects, uneven illumination, and other repeatable system influences. When these contributions are characterized, subsequent processing can distinguish persistent instrument behavior from meaningful structure, improving spatial accuracy and making quantitative measurements more dependable.
Optical approaches address artifacts before or during image formation through system design and control of the imaging path. Computational approaches act on recorded data using background correction, filtering, or reconstruction. Combining them can reduce the burden on either stage, but processing must remain selective: excessive correction may remove genuine detail or introduce bias into the final image and measurements.
The correction strategy must match the artifact source, its strength, and the spatial characteristics of the genuine structure. A method that removes glare, scattering, uneven background, or electronic noise should not flatten features that carry measurement information. Engineers therefore evaluate contrast, spatial accuracy, and processing-induced bias together rather than judging success solely by how clean the image appears.
A practical workflow starts by inspecting the image for aberration, glare, scattering, uneven illumination, or electronic noise and relating each pattern to a likely source. Engineers then select optical design changes, calibration, background correction, filtering, or computational reconstruction as appropriate. The corrected result is assessed for preserved detail, improved contrast, spatial accuracy, and measurement reliability before use in analysis.
The approach supports microscopy, machine vision, remote sensing, and biomedical imaging, where artifacts can interfere with inspection or quantitative analysis. In microscopy and biomedical systems, correction helps preserve meaningful structure; in machine vision, it supports dependable inspection; and in remote sensing, it improves the reliability of image-based interpretation. Across these applications, the goal is better measurement without processing-induced bias.