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27 February 2015 Improving wavelet denoising based on an in-depth analysis of the camera color processing
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Proceedings Volume 9400, Real-Time Image and Video Processing 2015; 94000Q (2015)
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
While Denoising is an extensively studied task in signal processing research, most denoising methods are designed and evaluated using readily processed image data, e.g. the well-known Kodak data set. The noise model is usually additive white Gaussian noise (AWGN). This kind of test data does not correspond to nowadays real-world image data taken with a digital camera. Using such unrealistic data to test, optimize and compare denoising algorithms may lead to incorrect parameter tuning or suboptimal choices in research on real-time camera denoising algorithms. In this paper we derive a precise analysis of the noise characteristics for the different steps in the color processing. Based on real camera noise measurements and simulation of the processing steps, we obtain a good approximation for the noise characteristics. We further show how this approximation can be used in standard wavelet denoising methods. We improve the wavelet hard thresholding and bivariate thresholding based on our noise analysis results. Both the visual quality and objective quality metrics show the advantage of the proposed method. As the method is implemented using look-up-tables that are calculated before the denoising step, our method can be implemented with very low computational complexity and can process HD video sequences real-time in an FPGA.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tamara Seybold, Mathias Plichta, and Walter Stechele "Improving wavelet denoising based on an in-depth analysis of the camera color processing", Proc. SPIE 9400, Real-Time Image and Video Processing 2015, 94000Q (27 February 2015);


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