Segmentation-based scale-invariant nonlocal means super resolution

Zooming in/out appears frequently in video shooting, which makes scale vary between frames. And object motion in videos may cause scale change of the object. It leads to the difficulty in finding similar patches and causes the invalidation of nonlocal means super resolution (NLM SR). In this paper, we propose a novel scale-compensated NLM SR algorithm. First, by considering the parameter model, the image is segmented in order to detect regions with different scales. Then, scale variations in different regions are computed based on SIFT descriptor. And patches extracted from different regions are compensated into the same scale to eliminate the effect of scale change. It is shown by experimental results that our proposed algorithm achieves the average PSNR by up to 0.678dB comparing with the state-of-the-art methods. Subjective results demonstrate the proposed method reduces artifacts and preserves more details.