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Compared to other methods, our algorithm is simple to implement and suitable for parallel processing.

Simulation results show that in contrast to previous methods our algorithm is much more effective and promising.

As is the case with all homogenization methods, our algorithm can be seen as a topology optimizer.

Comparing with many state-of-the-art methods, our algorithm strikes a good compromise between low-level and high-level noise estimations.

When compared to the traditional non-parallel methods, our algorithm can be expected to provide a more efficient segmentation on images with at least 13% improvement.

Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR).

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Compared to the state-of-the-art methods, our algorithms have acquired higher average cloud and snow/ice kappa values for different spatial resolutions.

According to the NSTD method, our algorithm shows more similarity to the tectonic map displayed in Fig. 9.

Since the RoIs generated by saliency method are less than those generated by select search method, our algorithm can greatly improve the detection precision and reduce the detection time than the other state-of-the-art algorithms, e.g., RCNN, Fast-RCNN, and Faster-RCNN.

Compared to previous methods, our proposed algorithm is performed very fast based on the simple ordinal measure of accumulated motion.

As there are a number of methods available in the literature (e.g.,[44 46]) to determine this number automatically, seeking full algorithm automation, we are also investigating the employment of some of these methods in our algorithm.

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