Abstract:
Hazy color images have distorted color contrast fields, making grayscale conversion difficult. This Image Processing Project proposes a new decolorization algorithm to convert hazy images into distortion-recovered grayscale images.
The CIELab color space relationship between the restored color contrast and its distorted input recovers the color contrast field. A nonlinear optimization problem is used to create the gray-scale image from this restoration.
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An extension of the Huber loss function solves this problem with a differentiable approximation solution. Experimental results show that the proposed algorithm preserves global luminance consistency while representing the original color contrast in gray-scales that are very close to the ground truth.
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Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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