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The paper introduces Colorist, a data‑augmentation method that uses classical statistical color matching to generate domain‑shifted medical images. By applying global mean‑standard‑deviation matching in RGB space, Colorist creates structurally intact variations without neural networks, outperforming deep generative models in fidelity and color alignment. Across multiple medical imaging datasets, it boosts balanced accuracy by up to 9% over state‑of‑the‑art domain‑generalization regularizers and 13% over no augmentation, while reducing computational cost and preserving anatomical structure.
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shift, structural damage, and over-exposure, which degrade the perceptual quality of the enhanced images.
arXiv:2609.25716v1 Announce Type: new Abstract: Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn ho...
arXiv:2610.32473v1 Announce Type: cross Abstract: Dominant color extraction is a fundamental task in computer vision, with applications in design, image retrieval, and content recommendation. However...
The paper presents a data‑driven method for estimating perceptual color differences by training regression models on human similarity judgments of 2,000 color pairs. Using COLIBRI fuzzy linguistic categories as features, linear regression achieves an R² of 0.595, outperforming RGB and HSI representations. The best result, an R² of 0.703, is obtained with LightGBM on a combined representation, showing that graded perceptual categories improve color‑difference prediction.
arXiv:2610.17661v2 Announce Type: replace Abstract: The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic...