Automating a Dehazing System by Self-Calibrating on Haze Conditions

Area of Science:

• Computer Vision
• Image Processing
• Artificial Intelligence

Background:

• Existing image dehazing algorithms often fail on haze-free images, producing artifacts.
• Current methods lack automatic haze density detection for arbitrary images.

Purpose of the Study:

• To develop an automated dehazing system for diverse image conditions.
• To enable robust dehazing regardless of the presence or density of atmospheric haze.

Main Methods:

• Simultaneous multiscale fusion-based dehazing and haze density estimation.
• Image blending guided by estimated haze density.
• Tone remapping for scene radiance restoration.

Main Results:

• The proposed system achieves satisfactory dehazing results on both hazy and haze-free images.
• Demonstrated superiority over state-of-the-art benchmark dehazing methods.
• Self-calibration capability for varying haze conditions.

Conclusions:

• The automated system effectively handles arbitrary images by integrating haze density estimation.
• The novel approach overcomes limitations of traditional two-stage dehazing techniques.
• This method offers a robust solution for image restoration in diverse atmospheric conditions.