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In this study, the researchers reported that their novel algorithm for hair restoration in dermoscopy images achieved high accuracy and texture preservation, outperforming other techniques in diagnostic accuracy and texture quality measures.

In this study, Gaussian process regression models combined with Grey Wolf optimization were used to predict and optimize phenolic and flavonoid content extraction from Carthamus caeruleus L. rhizomes, showing high accuracy and helping improve understanding and extraction processes through a new interactive tool.

This study presents a new approach to automatically remove hair artifacts from dermoscopic images, which reportedly performed well compared to existing methods like DullRazor using the PH2 datasets.

This study employed a laser-based method to analyze how light reflection patterns vary on human hair fibers, revealing changes related to hair color and the tilt of the cuticle cells.

This study found that the multi-thresholding method was the most effective for segmenting hair during laser removal, as it clearly distinguished hair patterns with minimal noise.