Now, the transform domain techniques in general, especially the wavelet-based video denoising selleck catalog methods, have been shown to outperform these spatiotemporal video denoising methods. Moreover, methods that combine spatiotemporal domain and transform domain were also proposed [13�C16], which could produce perfect denoising effect. Similarly, this kind of methods also require huge amount of computation.However, although video denoising technology has made great progress, most of these methods are unable to obtain ideal effect for large noisy video sequences in low light, which is urgently needed in many fields, especially in the security monitoring field. In this field, the monitoring devices are fixed in some places in general, so the captured video sequences have fixed background.

In practical applications, it often requires to see the characteristic both of still and moving objects in the video sequences clearly. This requirement can be met easily in the day time. However, in the night time, because of the low light condition, captured video sequences are contaminated by noise badly. To some extent, existing video denoising methods can reduce the noise of contaminated video sequences, but this is far from enough to meet the requirement.In this paper, a novel video denoising method based on a spatiotemporal Kalman-bilateral mixture model is proposed. Firstly, we perform an appropriate average filtering on current noisy frame to reduce the influence of noise, which we call prefiltering. This step is useless to the final denoising result, but preparative to the motion estimation.

Then, take advantage of the strong spatiotemporal correlations of neighboring frames, block-matching based motion estimation is performed by comparing current pre-filtered frame with previously denoised frames. Based on motion estimation results, current noisy frame is processed in the temporal domain by using Kalman filter [17] on the one hand. It is noteworthy that different blocks of the noisy frame have different filtering strength according to their block-matching results. In the Kalman filtering, motion blocks have quite weak filtering strength to keep their motion characteristic, while still blocks have strong filtering strength to reduce the noise. On the other hand, current noisy frame is also processed in the spatial domain by using bilateral filter [18], which aims at reducing the noise globally.

Finally, by weighting the two denoised frames from Kalman filtering and bilateral filtering, we can obtain a satisfactory result, in which the still region is largely from Kalman filtered result and the motion region is almost from bilateral filtered result. Experimental results show that the performance of our proposed method is effective over current competing video denoising methods.The remainder of the paper is organized as follows. Section 2 reviews related work. Section 3 describes our proposed spatiotemporal Drug_discovery Kalman-bilateral mixture model.