Foreground color prediction through inverse compositing

TL;DR

This paper introduces a recurrent neural network for natural image matting that estimates foreground and background colors from an initial alpha matte, enabling better compositions and user interaction.

Contribution

A novel recurrent neural network approach for recovering foreground and background colors in image matting, improving color estimation and user interaction capabilities.

Findings

Outperforms state-of-the-art color estimation methods

Enables easy user interaction for improved results

Provides superior color recovery in natural image matting

Abstract

In natural image matting, the goal is to estimate the opacity of the foreground object in the image. This opacity controls the way the foreground and background is blended in transparent regions. In recent years, advances in deep learning have led to many natural image matting algorithms that have achieved outstanding performance in a fully automatic manner. However, most of these algorithms only predict the alpha matte from the image, which is not sufficient to create high-quality compositions. Further, it is not possible to manually interact with these algorithms in any way except by directly changing their input or output. We propose a novel recurrent neural network that can be used as a post-processing method to recover the foreground and background colors of an image, given an initial alpha estimation. Our method outperforms the state-of-the-art in color estimation for natural image matting and show that the recurrent nature of our method allows users to easily change candidate solutions that lead to superior color estimations.