Photorealistic Style Transfer via Adaptive Filtering and Channel Seperation

Fei LUO

Abstract: The problem of color and texture distortion remains unsolved in the photorealistic style transfer task. It is mainly caused by the interference between color and texture during transferring. To address this problem, we propose an end-to-end network via adaptive filtering and channel separation. Given a pair of content image and reference image, we firstly decompose them into two structure layers through adaptive weighted least squares filter (AWLSF), which could better perceive the color structure and illumination. Then, we carry out RGB transfer in a channel separation way on the two generated structure layers. To deal with texture in a relatively independent manner, we use a module and a subtraction operation to get more complete and clear content features. Finally, we merge the color structure and texture detail into the ultimate result. We conduct solid quantitative experiments on four metrics NIQE, AG, SSIM, and PSNR, and make a user study. The experimental results demonstrate that our method is able to produce better results than previous state-of-the-art methods, and validate the effectiveness and superiority of our method.