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Related Experiment Videos

Nighttime Image Dehazing by Render.

Zheyan Jin1, Huajun Feng1, Zhihai Xu1

  • 1State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou 310027, China.

Journal of Imaging
|August 25, 2023
PubMed
Summary
This summary is machine-generated.

Nighttime image dehazing is challenging due to complex light interference. This study introduces a novel data synthesis and network improvement for more accurate and efficient nighttime fog removal.

Keywords:
data generationimage dehazeimage restoration

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Nighttime image dehazing is difficult due to uneven haze from artificial lights, causing atmospheric light, glow, and direct light interference.
  • Accurately distinguishing and removing complex scattering haze is challenging.
  • Acquiring high-definition nighttime fog removal datasets is problematic.

Purpose of the Study:

  • To develop an effective method for nighttime image dehazing.
  • To address the challenges of complex haze interference and data scarcity in nighttime scenes.
  • To improve the accuracy and efficiency of nighttime fog removal algorithms.

Main Methods:

  • Introduced a haze scattering formula to model 3D haze distribution.
  • Proposed a novel data synthesis method using CG textures and lumen lighting with ray tracing.
  • Converted 3D haze scattering to a 2D image dataset for neural network learning.
  • Improved a neural network and established a night haze intensity evaluation label based on optical PSF.

Main Results:

  • Achieved superior visual effects and objective indicators in nighttime dehazing experiments.
  • Demonstrated improved calculation speed compared to existing methods.
  • Successfully mapped 3D haze scattering relationships to a 2D image dataset for enhanced learning.

Conclusions:

  • The proposed data construction and network improvement effectively address nighttime image dehazing challenges.
  • The novel approach leads to significant improvements in visual quality and processing efficiency.
  • This work provides a robust solution for enhancing visibility in nighttime imagery.