Edit propagation is an important operation in image processing. Previous methods require that the samples selected interactively should have a sufficiently large number of colors. In this study

we propose a new robust edit propagation technique by efficient resampling

which may reduce the requirement of interactive operation. First

the image is segmented into superpixels and the center pixel of each superpixel is considered a new sample. Following the rule that each sample obeys one kind of editing mode

we calculate the edited color of each new sample by the similarity between the sample and editing modes and the similarity between those samples and its neighbors. The sample whose similarity to several editing modes is evidently high directly belongs to the corresponding editing mode. Otherwise

the editing mode of the current sample is determined by the neighboring samples with high similarities to the current sample. Then

the edited colors of all samples are propagated into the entire image based on the policy that similar colors are still similar after editing. Based on the similarity of the pixel and the samples

we select the number of samples adaptively for each pixel to reduce the interference of those samples with low similarity. Compared with previous methods

our solution is less sensitive to the input strokes. With the same simple strokes

our results exhibit better vision and details. The method can effectively simplify the user interaction by replacing the sparse input samples with many colors resamples. Moreover

the experiments and comparisons show that the method can preserve the edited colors and its details.