Recoloring Cartoon Images Based on Palette Mapping Using K-Means Clustering and Gradient Analysis

This study introduces a palette-based method for the recolorization of cartoon images by combining the k-means clustering algorithm and gradient analysis. The method aims to preserve the visual identity of the original image while allowing flexibility in color manipulation. By segmenting colors using k-means clustering, the approach produces a representative color palette reflecting the dominant colors in the image. Gradient analysis is then applied to maintain smooth color transitions and aesthetic consistency. This palette serves as the basis for the recolorization process, enabling intuitive color adjustments without disrupting the visual structure of the image. Experimental results demonstrate that this method can produce recolorized images with high visual quality, preserve original image details, and provide users with greater control over the resulting colors.