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Multi-Scale Image Defogging Network Based on Cauchy Inverse Cumulative Function Hybrid Distribution Deformation

Lu Ji1, Chao Chen1

  • 1College of Aeronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210006, China.

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Summary
This summary is machine-generated.

This study introduces a novel defogging algorithm using Cauchy distribution convolutions to improve performance in extreme fog. The new method enhances image clarity and detail, outperforming existing techniques in dense fog conditions.

Keywords:
Cauchy distributionattention mechanismdeformable convolutionimage defogginginverse Cauchy integral function

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Existing defogging algorithms suffer performance degradation in extreme fog due to limitations in modeling outliers.
  • Taylor series-based deformable convolutions exhibit local approximation errors, failing to capture sudden changes in fog density.

Purpose of the Study:

  • To develop an advanced defogging algorithm capable of handling extreme fog conditions and improving image quality.
  • To address the limitations of traditional methods by incorporating the Cauchy distribution for better outlier modeling.

Main Methods:

  • A displacement generator using the inverse cumulative distribution function (ICDF) of the Cauchy distribution, including a novel double-peak Cauchy ICDF for dynamic balancing.
  • A Cauchy-Gaussian fusion module for adaptive learning of hybrid coefficients to balance smooth regions and edge details.
  • Tree-based multi-path and cross-resolution feature aggregation with adjustable window sizes for local-global feature fusion.

Main Results:

  • Achieved a 2.26 dB improvement in peak signal-to-noise ratio (PSNR) over TaylorV2 expansion attention mechanism on the RESIDE dataset.
  • Demonstrated an 0.88 dB PSNR improvement in heavily hazy regions (fog concentration > 0.8).
  • Ablation studies confirmed the effectiveness of Cauchy distribution convolution in dense fog and varied lighting.

Conclusions:

  • The proposed Cauchy-based defogging method significantly enhances performance in extreme fog conditions.
  • Introduced a novel attention mechanism and multi-path encoding approach, offering a new theoretical perspective for computer vision tasks.
  • The method effectively models outliers and dynamically balances feature representation for improved defogging results.