基于整体和非局部低秩分解的视频脉冲噪声去除方法

Video impulse noise removal method based on global and non-local low rank decomposition

视频存在着整体关联性和基于图像块的非局部关联性。针对现有的视频恢复方法仅仅利用一种尺度的关联性质,从而限制了算法恢复性能的问题,通过考虑这两种低秩性质,提出了基于整体关联性和非局部关联性的视频恢复算法。首先,利用视频帧的整体关联性把被噪声污染的视频分解为整体低秩成分和稀疏余项成分。然后,对于余项视频部分其相邻帧存在非局部关联性,利用基于k维树的非局部技术组成低秩图像块组,并通过低秩分解模型去除图像块噪声。最后,整合整体低秩部分与处理后的余项部分,从而得到准确的视频恢复结果。在去除视频中脉冲噪声的实验中,所提算法与联合稀疏与低秩分解算法相比平均峰值信噪比(PSNR)提高了1.3 dB,与鲁棒时空分解算法相比PSNR提高了2 dB。实验结果表明了所提算法的有效性和优越性。

Videos possess global correlation and non-local correlation based on image patches.Concerning the problem that existing video restoration methods only utilize one of these correlation properties,which limits the performance of video restoration algorithms,by considering these two low rank properties,a new video restoration algorithm based on global correlation and non-local correlation of video data was proposed.Firstly,the long-term global correlation of video frames was used to decompose the video corrupted by noise into global low-rank components and sparse residuals.Secondly,for residual part,there is non-local correlation between adjacent frames,non-local technique based on k-dimensional tree was utilized to form a group of similar patches,then low-rank decomposition model was used to process a group of image patches so that noise can be removed to obtain a clean image patch structure.Finally,the global low-rank part was added to the processed residual part to obtain a clean image.In the experiment of removing impulse noise from the noisy videos,the average Peak Signal-to-Noise Ratio(PSNR)of the proposed algorithm is 1.3 dB higher than that of joint low rank and sparse aprroximation algorithm and 2 dB higher than that of robust temporal-spatial decomposition method.The experimental results show that the proposed algorithm is effective and superior.