Intrinsic Decomposition with Robustly Separating and Restoring Colored Illumination.

Abstract

Intrinsic decomposition separates an image into reflectance and shading, which contributes to image editing, augmented reality, etc. Despite recent efforts dedicated to this field, effectively separating colored illumination from reflectance and correctly restoring it into shading remains an challenge. We propose a deep intrinsic decomposition method to address this issue. Specifically, by transforming intrinsic decomposition process in RGB image domains into the combination of intensity and chromaticity domains, we propose a novel macro intrinsic decomposition network framework. This framework enables the generation of finer intrinsic components through more relevant features propagation and more detailed sub-constraints guidance. In order to expand the macro network, we integrate multiple attention mechanism modules in key positions of encoders, which enhances the extraction of distinct features. We also propose a skip connection module based on specific deep features guidance, which can filter out features that are physically irrelevant to each intrinsic component. Our method not only outperforms state-of-the-art methods across multiple datasets, but also robustly separates illumination from reflectance and restores it into shading in various types of images. By leveraging our intrinsic images, we achieve visually superior image editing effects compared to other methods, while also being able to manipulate the inherent lighting of the original scene.