Abstract
ABSTRACT Nuclear radiation-contaminated video deblurring is an important issue of the robot vision system and has been widely studied. In this paper, a hybrid radiation-contaminated video frame enhancement algorithm is proposed that utilizes both intra-frame and inter-frame correlation by a two-stage strategy. In the first stage, total variation (TV) transformation are used to locate the spot areas, and then local TV is employed to restore spot areas. The preliminary deblurring result not only enhances the video frame and similar patch matching accuracy but also provides reliable estimates of filtering parameters. In the second stage, visual group technology and improved k-nearest neighbours (k-NN) method is used to select similar frames and reference patches respectively. The final enhanced video frame is obtained by a novel patch-based group sparse method. Experimental results clearly show that the proposed method outperforms other state-of-the-art methods in both quantitative evaluation indices and visual quality measurements.
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