Improved color patch similarity measure based weighted median filter

Abstract

Median filtering the intermediate flow fields during optimization has been demonstrated to be very useful for improving the estimation accuracy. By formulating the median filtering heuristic as non-local term in the objective function, and modifying the new term to include flow and image information that according to spatial distance, color similarity as well as the occlusion state, a weighted non-local term (a practical weighted median filter) reduces errors that are produced by median filtering and better preserves motion details. However, the color similarity measure, which is the most powerful cue, can be easily perturbed by noisy pixels. To increase robustness of the weighted median filter to noise, we introduce the idea of non-local patch denoising method to compute the color similarity in terms of patch difference. Most importantly, we propose an improved color patch similarity measure (ICPSM) to modify the traditional patch manner based measure from three aspects. Comparative experimental results on different optical flow benchmarks show that our method can denoise the flow field more effectively and outperforms the state-of-the art methods, especially for heavy noise sequences.