Pseudo-Motion-Compensated Impulse Noise Removal from Color Video

In order to take into account the both objects of noise suppression and detail preservation, the switching image denoising method is widely used by academics. Moreover, the strong correlation between adjacent frames, especially the accurate motion-prediction compensation, is also introduced into the video denoising scheme. While these compounded methods produce good performance, they are either unsuited for hardware implementation due to complicated transformation or time consuming due to large-range search. These defects limit the usage of new results in rapid practical applications of video. This thesis proposed a novel Pseudo-Motion-Compensated Denoising Algorithm (PMCDA) to achieve a better trade-off between the performance and the efficiency during the noise suppression process of the video. First, the simple peer-group detection method is extended to a 3D version to accelerate the pre-detection process and ignore the precisely noise-free pixels. It’s noted that neither any sort nor move compensation is needed in the 3D peer-group detection. Therefore, the fast pre-detection stage can significantly reduce the overall cost of computing since low-noise contamination is more common in real-life videos. In the high-noise circumstance, PMCDA can maximize the utilization of the detection. The remaining noise candidates will be filtered via a sequence according to their 3D noise ratio. The more reliable area has higher priority in process to get better restoration. It can also achieve a similar effect as that of recursive method by only using a cost of single round and improve the border damage by providing more reliable references to high-noise area. Furthermore, based on the pre-detection results, we can also evaluate the rough noise ratio of video and to decide the appropriate filter for the second stage. In particular, we propose a new, simple and fast pseudo-motion-compensation methodology. As a result, the new method does achieve better performance and efficiency tradeoffs. The experimental simulations show that the proposed video denoising algorithm still outperforms some other state-of-the-art methods while exhibiting the advantages of fast computation.