動態式區塊色調調整實現臉部真實感強化

Abstract

Realizing non-real face images is a challenging problem. Different artists may have different painting styles. When seeing a non-real image, people may enjoy the diverse looks of face features. However, when looking at a real photo, they are sensitive to the face contours and feature details. How to transform sparse stroke of paintings into real-face features makes the problem highly difficult. We propose an example-based method to enhance the realism of painted portraits. We use graph-cut based optimization to synthesize a face from multiple sources and form a piece-up face. Next, a quadratic optimization is applied to close the color differences of adjacent patches. In the end of our system, we seamlessly stitch these patches by a two-level blending process. Our experiments show that the proposed method is able to generate a realistic and novel result.

Realizing non-real face images is a challenging problem. Different artists may have different painting styles. When seeing a non-real image, people may enjoy the diverse looks of face features. However, when looking at a real photo, they are sensitive to the face contours and feature details. How to transform sparse stroke of paintings into real-face features makes the problem highly difficult. We propose an example-based method to enhance the realism of painted portraits. We use graph-cut based optimization to synthesize a face from multiple sources and form a piece-up face. Next, a quadratic optimization is applied to close the color differences of adjacent patches. In the end of our system, we seamlessly stitch these patches by a two-level blending process. Our experiments show that the proposed method is able to generate a realistic and novel result.