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Spatial-temporal noise reduction method optimized for real-time implementation

Image de-noising in the spatial-temporal domain has been a problem studied in-depth in the field of digital image processing. However complexity of algorithms often leads to high hardware resource usage, or computational complexity and memory bandwidth issues, making their practical use impossible. In our research we attempt to solve these issues with an optimized implementation of a practical spatial-temporal de-noising algorithm. Spatial-temporal filtering was performed in Bayer RAW data space, which allowed us to benefit from predictable sensor noise characteristics and reduce memory bandwidth requirements. The proposed algorithm efficiently removes different kinds of noise in a wide range of signal to noise ratios. In our algorithm the local motion compensation is performed in Bayer RAW data space, while preserving the resolution and effectively improving the signal to noise ratios of moving objects. The main challenge for the use of spatial-temporal noise reduction algorithms in video applications is the compromise between the quality of the motion prediction and the complexity of the algorithm and required memory bandwidth. In photo and video applications it is very important that moving objects should stay sharp, while the noise is efficiently removed in both the static background and moving objects. Another important use case is the case when background is also non-static as well as the foreground where objects are also moving. Taking into account the achievable improvement in PSNR (on the level of the best known noise reduction techniques, like VBM3D) and low algorithmic complexity, enabling its practical use in commercial video applications, the results of our research can be very valuable.