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.

摘要

核辐射污染视频去模糊是机器人视觉系统的一个重要问题，得到了广泛的研究。在本文中，提出了一种混合辐射污染视频帧增强算法，该算法通过两阶段策略同时利用帧内和帧间相关性。在第一阶段，使用全变分（TV）变换来定位斑点区域，然后使用局部电视来恢复斑点区域。初步的去模糊结果不仅提高了视频帧和相似块匹配的准确性，而且还提供了过滤参数的可靠估计。在第二阶段，视觉组技术和改进的k-最近邻（k-NN）方法分别用于选择相似帧和参考块。最终增强的视频帧是通过一种新颖的基于补丁的组稀疏方法获得的。实验结果清楚地表明，所提出的方法在定量评估指标和视觉质量测量方面均优于其他最先进的方法。