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Related Concept Videos

Active Filters01:25

Active Filters

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:
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category, whereas...

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Related Experiment Video

Updated: May 12, 2026

A Simple Technique to Assay Locomotor Activity in Drosophila
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A Simple Technique to Assay Locomotor Activity in Drosophila

Published on: February 24, 2023

Guided image filtering.

Kaiming He1, Jian Sun, Xiaoou Tang

  • 1Visual Computing Group, Microsoft Research Asia, Microsoft Building 2, #5 Dan Leng Street, Hai Dian District, Beijing 100080, China. kahe@microsoft.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 20, 2013
PubMed
Summary
This summary is machine-generated.

We introduce the guided filter, a novel image filter that preserves edges effectively. This fast, efficient filter enhances various computer vision tasks like image smoothing and dehazing.

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Area of Science:

  • Computer Vision and Graphics
  • Image Processing

Background:

  • Edge-preserving smoothing is crucial for image processing.
  • Existing filters like the bilateral filter have limitations near edges.

Purpose of the Study:

  • To propose a novel explicit image filter, the guided filter.
  • To demonstrate its effectiveness and efficiency in various computer vision applications.

Main Methods:

  • The guided filter is derived from a local linear model.
  • It utilizes a guidance image, which can be the input image or another image.
  • It employs a fast, non-approximate linear time algorithm.

Main Results:

  • The guided filter exhibits superior performance near edges compared to the bilateral filter.
  • It enables structure transfer from a guidance image for applications like dehazing and guided feathering.
  • It is one of the fastest edge-preserving filters available.

Conclusions:

  • The guided filter is a versatile and efficient tool for a wide range of computer vision and graphics applications.
  • Its ability to preserve edges and transfer structures offers significant advantages.
  • It provides a fast and effective solution for tasks including smoothing, detail enhancement, and image matting.