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Temporal accumulation of images is a well-known approach to improve signal to noise ratios of still images taken in a low light conditions. However, the complexity of known algorithms often leads to high hardware resource usage, increased memory bandwidth and computational complexity, making their practical use impossible. In our research we attempt to solve this problem with an implementation of a practical spatial-temporal de-noising algorithm, based on image accumulation. Image matching and spatial-temporal filtering was performed in Bayer RAW data space, which allowed us to benefit from predictable sensor noise characteristics, thus allowing using a range of algorithmic optimizations. The proposed algorithm accurately compensates for global and local motion and efficiently removes different kinds of noise in noisy images taken in low light conditions. In our algorithm we were able to perform global and local motion compensation in Bayer RAW data space, while preserving the resolution and effectively improving signal to noise ratios of moving objects as well as non-stationary background. The proposed algorithm is suitable for implementation in commercial grade FPGA’s and capable of processing 16MP images at capturing rate (10 frames per second). The main challenge for matching between still images is the compromise between the quality of the motion prediction and the complexity of the algorithm and required memory bandwidth. Still images taken in a burst sequence must be aligned to compensate for background motion and foreground objects movements in a scene. High resolution still images coupled with significant time between successive frames can produce large displacements between images, which creates additional difficulty for image matching algorithms. In photo applications it is very important that the noise is efficiently removed in both static, and non-static background as well as in a moving objects, maintaining the resolution of the image. In our proposed algorithm we solved the issue of matching current image with accumulated image data in Bayer RAW data space in order to efficiently perform spatio-temporal noise reduction and reduce the computational requirements. In this paper we provide subjective experimental results to demonstrate the ability of the proposed method to match noisy still images in order to perform efficient de-noising and avoid motion artefacts in resulting still images.
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