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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.
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Efficient Multiscale Gradient-Domain Filtering for Image and Video Dehazing with Enhanced Temporal Coherence.

Neelam Kumari1, Isha Kansal1, Preeti Sharma2

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This study introduces an efficient multiscale gradient-domain filter for image and video dehazing. The method enhances clarity and reduces artifacts, improving visual quality for real-time applications.

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Atmospheric scattering degrades image clarity, posing challenges for computer vision tasks.
  • Existing dehazing methods often exhibit high computational costs and can lose important gradient details.
  • Video dehazing specifically suffers from flickering artifacts, limiting real-world applicability.

Purpose of the Study:

  • To propose an efficient and high-quality dehazing technique for both images and videos.
  • To address limitations of existing methods, including computational cost, detail loss, and flickering artifacts.
  • To enhance texture and edge retention while maintaining temporal coherence in video dehazing.

Main Methods:

  • A multiscale gradient-domain weighted guided image filter (GWGIF) is proposed for refined transmission map estimation.
  • Minimum Preserving Subsampling (MPS) is utilized for efficient atmospheric parameter estimation and complexity reduction.
  • A Gradient-Based Correlation Factor (GCF) is introduced to mitigate flickering artifacts in video dehazing.

Main Results:

  • The proposed method achieved superior perceptual quality with PIQE, NIQE, and BRISQE scores of 26.98, 2.78, and 20.18, respectively.
  • High temporal coherence was demonstrated in video dehazing with a Mean Square Error (MSE) deviation of 0.003.
  • Significant reduction in flickering artifacts was observed in video dehazing compared to existing approaches.

Conclusions:

  • The proposed GWGIF-based dehazing technique offers enhanced efficiency and visual quality for both image and video processing.
  • The method effectively preserves gradient details and improves texture/edge retention.
  • Its suitability for real-time applications like autonomous driving and surveillance is highlighted due to its performance and temporal stability.