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A real-time video denoising algorithm with FPGA implementation for Poisson–Gaussian noise

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Abstract

The denoising function in digital imaging devices must consider resource consumption and real-time capability in addition to effective noise-removal performance. One commonly used denoising method is pixel similarity weighted frame averaging (PSWFA). In this study, we improve the denoising capability of PSWFA using a pre-filter that consists of a downsampling operator and a small Gaussian filter. Moreover, given that noise in digital imaging devices is signal dependent and is typically modeled as a Poisson–Gaussian distribution, we introduce generalized Anscombe transformation to remove the signal dependency by rendering the noise variance constant. The transformed image can be considered corrupted by an approximately Gaussian noise. To embed our algorithm in hardware, we implement our algorithm on a Spartan-6 FPGA for evaluation. We also compare our algorithm with some existing denoising methods on FPGA. For further evaluation of the denoising ability, the algorithm is compared with some state-of-the-art algorithms that are not implemented on FPGA but have high performance on a personal computer. Experimental results on both simulated noise videos and actually captured low-light noise videos show the effectiveness of our algorithm, particularly in the processing of large-scale noise.

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Acknowledgments

This research was partially supported by the National Natural Science Foundation (NSFC) of China (Grant Nos. 61175006 and 61175015).

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Authors and Affiliations

  1. College of Information System and Management, National University of Defense Technology, Changsha, 410073, Hunan, China

    Xin Tan, Yu Liu, Chenglin Zuo & Maojun Zhang

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  1. Xin Tan
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  2. Yu Liu
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  3. Chenglin Zuo
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  4. Maojun Zhang
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Correspondence to Xin Tan.

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Tan, X., Liu, Y., Zuo, C. et al. A real-time video denoising algorithm with FPGA implementation for Poisson–Gaussian noise. J Real-Time Image Proc 13, 327–343 (2017). https://doi.org/10.1007/s11554-014-0405-2

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  • DOI: https://doi.org/10.1007/s11554-014-0405-2

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