Adaptive Multilevel Coloring and Significant Texture Selecting for Automatic Deep Learning Image Transfer

Abstract

This paper proposes an image style transfer technique based on target image color and style, which improves the limitations of previous studies that only consider inter-image color transfer and use only deep learning for style transfer. First, an adaptive multilevel cut is made based on the luminance distribution of the two image pixels, and then a color transfer is applied to each region. Next, deep learning is used to select effective features for the target image, and the convolutional layer determines the extent of effective features by using the structural similarity index (SSIM) and black blocks. Selecting a convolutional layer with more effective features can reduce the limitations of the deep learning style transfer that requires artificial control parameters. The proposed method improves image quality by automatically simulating the color and style of the target image and controlling the parameters without human intervention. By evaluating the similarity between the result image and the target image, the proposed method can reduce the gap of variance by more than two times, and the result image can display a balance between the color and style of the target image.