Efficient video denoising based on dynamic nonlocal means ?
A video denoising algorithm, which is based on dynamic nonlocal means (DNLM), is developed. Firstly, the standard nonlocal means and Kalman filtering are reviewed briefly. Then, using the idea of nonlocal means and linear minimum variance fusion, a weighted translational motion model without the explicit motion estimation and a weighted translational observation model are proposed to modify the state transition and observation equations. Finally, the overall dynamic denoising algorithm under the Kalman filter framework is presented. The main contribution of our work is a dynamic nonlocal means algorithm that is developed for video denoising under the Kalman filtering framework. In this algorithm, all computations are pixel-wise and it is easy to realize an efficient recursive algorithm for real-time processing. Experimental results for different test videos demonstrate the power of proposed method based on peak signal-to-noise-ratio (PSNR), structural similarity (SSIM) and motion-based video integrity evaluation index (MOVIE). The proposed method performs better than SNLM with the average PSNR gain of 2.33dB, and outperforms SEQWT, 3DWTF and IFSM with the average SSIM gains of 0.033, 0.0087 and 0.049. It has competitive performance with STA, WRSTF and 3DSWDCT, but needs lower computational cost. Though the proposed DNLM is not competitive with several state-of-the-art video denoising algorithms such as VBM3D, K-SVD, 3D-Patch, and ST-GSM, it may be anyway valuable to readers working in this field as a source of inspiration for their further researches.
video denoising nonlocal means kalman filtering linear minimum variance fusion