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Computing the gradient of an image is a common step in computer visionpipelines. The image gradient quantifies the magnitude and direction of edgesin an image and is used in creating features for downstream machine learningtasks. Typically, the image gradient is represented as a grayscale image. Thispaper introduces directional pseudo-coloring, an approach to color the imagegradient in a deliberate and coherent manner. By pseudo-coloring the imagegradient magnitude with the image gradient direction, we can enhance the visualquality of image edges and achieve an artistic transformation of the originalimage. Additionally, we present a simple style transfer pipeline which learns acolor map from a style image and then applies that color map to color the edgesof a content image through the directional pseudo-coloring technique. Code forthe algorithms and experiments is available athttps://github.com/shouvikmani/edge-colorizer.

Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring