A new sampling algorithm for high-quality image matting

• Multi-scale edge-preserving decomposition model based on WLS filter is used for sharping the boundary between foreground and background, especially for images that foreground and background are similar.
• A new innovative criterion is used to consider all the useful samples so that it would avoid the overlooking of some important candidate pixels.
• A more convincing database is used to check the effectiveness of our model.

Image matting is the extraction of the foreground from an image through the use of provided information. It has been an important technique in the image and video editing field. Current image matting methods estimate the foreground and background, based on information provided regarding the nearby pixels. Color sampling has been an effective means for matting directly, and quite a few methods have achieved high quality matting results based on color sampling. However, there are some drawbacks; for example it is easy to overlook important candidate pixels for matting, and even if the candidate pixels are effectively selected, similar foregrounds and backgrounds will reduce the accuracy of the matting.In this paper’s work, a Weighted-Least Squares (WLS) filter was utilized to sharpen the boundaries between the foregrounds and backgrounds, which facilitated the matting process; and an innovative sampling criterion based on random searching for the matting was then presented. This innovative method could effectively prevent valid samples being overlooked, and could manage the relationships of the nearby and distant pixels. In this process, a new cost function was utilized to evaluate the candidate samples. Experiments utilizing an image database demonstrated that this method significantly improved the matting results.