Year: 2025
Type: article
Abstract: Variations in illumination conditions critically degrade color fidelity in digital images, thereby compromising the accuracy of downstream computer vision tasks. Building upon these historical principles, this paper proposes a self-attention autoencoding feature support vector regression algorithm. The method extracts probability distributions in the luminance-red-green color space as primitive features, reconstructs them through a self-attention augmented autoencoder, and deploys support vector regression for illumination estimation. Experimental validation demonstrates superior robustness against noise and illumination diversity compared to feature-based alternatives. On the linear GreyBall SFU dataset, our method achieves an average 64.4% reduction across six key error metrics relative to the minimum values from all eight comparative methods. On the Cube++ dataset, it yields an average 44.9% reduction across six error metrics relative to the minimum values from all six comparative methods.
DOI: 10.1364/josaa.562543
Authors: Zhijie Huang, Tong Wu, Tianze Cui, Xufen Xie
Institution: Shenyang Aerospace University
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Award: G6271208938
OpenAlex ID: W4413314534