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Related Concept Videos

Deconvolution01:20

Deconvolution

293
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
293

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Related Experiment Video

Updated: Oct 12, 2025

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
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Region Adaptive Single Image Dehazing.

Changwon Kim1

  • 1Korean Intellectual Property Office, Daejeon 35208, Korea.

Entropy (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

This study introduces a novel image dehazing method combining dark channel prior (DCP) and bright channel prior (BCP) to improve visibility in foggy or snowy conditions. The new approach enhances image quality by reducing color distortions, outperforming existing techniques.

Keywords:
Shannon’s entropybright channel priordark channel priordehazetexture probability

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Outdoor images suffer from poor visibility due to atmospheric conditions like fog and snow.
  • Dark Channel Prior (DCP) is a popular but limited dehazing technique, often causing color distortions in bright areas.

Purpose of the Study:

  • To develop an improved single-image dehazing method overcoming DCP limitations.
  • To enhance visibility and reduce color distortions in adverse weather images.

Main Methods:

  • A novel method combining Dark Channel Prior (DCP) and Bright Channel Prior (BCP).
  • Patch-based robust atmospheric light estimation for region-specific prior application.
  • Region adaptive haze control using local image entropy to differentiate flat and texture regions.

Main Results:

  • The proposed method effectively reduces color distortions in bright regions.
  • Improved visibility in textured regions compared to traditional DCP methods.
  • Outperforms state-of-the-art methods both visually and numerically on synthetic and real datasets.

Conclusions:

  • The combined DCP and BCP approach offers superior image dehazing performance.
  • Region adaptive control is crucial for handling diverse image characteristics.
  • The method provides a robust solution for enhancing visibility in challenging weather conditions.