Existing matting methods focus on improving matte quality to produce high-quality composites. This generally requires significant manual interaction, a tedious task for the user. Despite these efforts, the composites may still exhibit evident artifacts, especially in the case of transparent and complicated objects as their related pixels always contain percentage of the background. In this paper, we focus on the enhanced use of mattes to produce satisfactory composites by suppressing the discrepancies around objects of interest. This approach is motivated by cloning methods but overcomes their shortcoming of ineffective treatment of the over-included regions around objects of interest. For this, we present an enhanced matting function by including a term to smooth the local contrasts for seamless composition, and meanwhile, we develop a novel algorithm to generate mattes with reduced user interaction and improved usability. As a result, we reduce the composite's dependence on the user's input and only require the user to drag a box to enclose the objects of interest. As shown in the user studies and the experimental results, our method requires many times less user interaction than the existing matting methods and cloning methods. Our method is more effective in producing good composites in a simple interactive manner, especially when treating transparent and complicated objects, thereby providing a superior approach for image composition.

In propagating edits for image editing, some edits are intended to affect limited local regions, while others act globally over the entire image. However, the ambiguity problem in propagating edits is not adequately addressed in existing methods. Thus, tedious user input requirements remain since the user must densely or repeatedly input control samples to suppress ambiguity. In this paper, we address this challenge to propagate edits suitably by marking edits for local or global propagation and determining their reasonable propagation scopes automatically. Thus, our approach avoids propagation conflicts, effectively resolving the ambiguity problem. With the reduction of ambiguity, our method allows fewer and less-precise control samples than existing methods. Furthermore, we provide a uniform framework to propagate local and global edits simultaneously, helping the user to quickly obtain the intended results with reduced labor. With our unified framework, the potentially ambiguous interaction between local and global edits (evident in existing methods that propagate these two edit types in series) is resolved. We experimentally demonstrate the effectiveness of our method compared with existing methods.

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.

Abstract It is popular to edit the appearance of images using strokes, owing to their ease of use and convenience of conveying the user's intention. However, propagating the user inputs to the rest of the images requires solving an enormous optimization problem, which is very time consuming, thus preventing its practical use. In this paper, a two‐step edit propagation scheme is proposed, first to solve edits on clusters of similar pixels and then to interpolate individual pixel edits from cluster edits. The key in our scheme is that we use efficient stroke sampling to compute the affinity between image pixels and strokes. Based on this, our clustering does not need to be stroke‐adaptive and thus the number of clusters is greatly reduced, resulting in a significant speedup. The proposed method has been tested on various images, and the results show that it is more than one order of magnitude faster than existing methods, while still achieving precise results compared with the ground truth. Moreover, its efficiency is not sensitive to the number of strokes, making it suitable for performing dense edits in practice.