Edge-aware Gradient Domain Optimization Framework for Image Filtering by Local Propagation
Gradient domain methods are popular for image processing. However, these methods even the edge-preserving ones cannot preserve edges well in some cases. In this paper, we present new constraints explicitly to better preserve edges for general gradient domain image filtering and theoretically analyse why these constraints are edge-aware. Our edge-aware constraints are easy to implement, fast to compute and can be seamlessly integrated into the general gradient domain optimization framework. The improved framework can better preserve edges while maintaining similar image filtering effects as the original image filters. We also demonstrate the strength of our edge-aware constraints on various applications such as image smoothing, image colorization and Poisson image cloning.
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