Related Experiment Video
Updated: Jan 19, 2026
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Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
Published on: July 11, 2025
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Hazy Image Decolorization with Color Contrast Restoration
Summary
This study introduces a novel algorithm for converting hazy color images to grayscale. The method effectively restores distorted color contrast, producing a high-quality grayscale image that preserves luminance and original details.
Area of Science:
- Computer Vision
- Image Processing
- Digital Image Restoration
Background:
- Hazy color images present challenges for grayscale conversion due to distorted color contrast.
- Existing methods struggle to accurately recover the original color contrast in grayscale images.
Purpose of the Study:
- To propose a novel decolorization algorithm for transforming hazy color images into distortion-recovered grayscale images.
- To effectively recover the color contrast field distorted by haze.
Main Methods:
- A relationship between restored and distorted color contrast is defined in the CIELab color space.
- A nonlinear optimization problem is formulated to construct the grayscale image.
- A differentiable approximation solution using an extended Huber loss function is introduced.
Main Results:
- The algorithm effectively preserves global luminance consistency.
- The resulting grayscale images accurately represent the original color contrast.
- The output is visually and quantitatively close to ground truth grayscale images.
Conclusions:
- The proposed algorithm successfully converts hazy color images to high-fidelity grayscale images.
- It offers an effective solution for preserving luminance and color contrast information during decolorization.
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