Guided Image Filter
Guided Image Filter - Related Videos
01:27
Passive Filters
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2024
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources. Low-Pass Filters Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff frequency...
09:32
Evaluating the Effect of Coated Filters and Pre-Filters on the Infectivity and Concentration of Aerosolized Phi6 Virus
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2026
Here, we present a study to evaluate the anti-viral efficacy of copper- and silver-based surface coatings on air filters. Using a bioaerosol chamber and Phi6 bacteriophage, this method quantifies viral inactivation and mechanical filtration efficiency for self-sanitizing indoor air technologies.
01:25
Active Filters
0 Views •
2025
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is: Where Af is the gain at low frequencies also known as the dc gain, s is the...
10:53
Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
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Cited by 10 •
2017
This protocol outlines the implementation of image-guided, laser-based hydrogel degradation to fabricate vascular-derived, biomimetic microfluidic networks embedded in poly(ethylene glycol) diacrylate (PEGDA) hydrogels. These biomimetic microfluidic systems may be useful for tissue engineering applications, generation of in vitro disease models, and fabrication of advanced "on-a-chip" devices.
09:05
Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices
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Cited by 11 •
2017
This protocol describes how to conduct automatic image-guided patch-clamp experiments using a system recently developed for standard in vitro electrophysiology equipment.
FAQs
Within each neighboring window, the filter represents the output using a linear function of the guidance image. It estimates the function’s coefficients through regularized least-squares fitting, which balances agreement with the image data against unstable or overly sensitive coefficient estimates. Repeating this local estimation across windows produces a spatially adaptive transformation rather than one uniform operation.
The guidance image supplies the structural information used during local fitting. When it contains a distinct edge, the estimated relationship changes between neighboring regions, limiting smoothing across that boundary. This allows noise or small variations to be reduced within similar areas while retaining important structural transitions, a property that supports clearer outputs in machine-vision and imaging systems.
Regularization is included in the least-squares estimation used to obtain local coefficients. Its role is to constrain the fitting process so that the locally modeled relationship remains stable instead of responding excessively to image variations. This supports consistent smoothing or transformation across neighboring windows while preserving the guidance-defined structure that the filter is intended to retain.
An application begins by selecting the image to be processed and, when needed, a related guidance image such as color data for depth data. The method then examines neighboring windows, estimates local linear coefficients with regularized least squares, and applies the resulting relationships to produce the output. The same workflow can support smoothing, enhancement, or contrast adjustment.
Joint filtering is useful when one image provides structural guidance for another related image. The overview specifically identifies color and depth data as an example, allowing information from the visually detailed image to guide processing of the corresponding data. This relationship supports depth upsampling and can improve structural consistency without treating both inputs as identical signals.
Guided image filtering is used in computational photography, machine vision, image fusion, and depth upsampling. Across these applications, its value comes from reducing unwanted variation or modifying image appearance while maintaining structural features. It can therefore support visual quality improvements, combine related image information, and preserve boundaries important for interpretation by imaging systems.