Neural Artistic Style and Color Transfer Using Deep Learning
This work addresses artists and designers by improving artistic expression through enhanced style and color transfer, but it is incremental as it builds on existing neural style transfer and color transfer techniques.
The paper tackles the problem of combining neural artistic style transfer with color transfer to enhance visual outputs, introducing a method that uses KL divergence to evaluate several color matching algorithms and showing that IDT with regrain achieves the lowest KL divergence of 0.15, indicating better color fidelity.
Neural artistic style transfers and blends the content and style representation of one image with the style of another. This enables artists to create unique innovative visuals and enhances artistic expression in various fields including art, design, and film. Color transfer algorithms are an important in digital image processing by adjusting the color information in a target image based on the colors in the source image. Color transfer enhances images and videos in film and photography, and can aid in image correction. We introduce a methodology that combines neural artistic style with color transfer. The method uses the Kullback-Leibler (KL) divergence to quantitatively evaluate color and luminance histogram matching algorithms including Reinhard global color transfer, iteration distribution transfer (IDT), IDT with regrain, Cholesky, and PCA between the original and neural artistic style transferred image using deep learning. We estimate the color channel kernel densities. Various experiments are performed to evaluate the KL of these algorithms and their color histograms for style to content transfer.