Abstract
Denoising is an intensively studied topic in the recent years. Improved as the denoising algorithms, they are not practical in use. In this research, we try to develop a robust, accurate and fast real noisy image denoising algorithm. Firstly, we studied the Additive White Gaussian Noise (AWGN) and then extended our reasearch to the practical signal-dependent noise.
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Noise level estimation for Additive White Gaussian Noise (AWGN) AWGN noise model is widely used in the denoising literture, however, there are few research about the estimation of noise standard deviation. |
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Optimal noise level parameter for blind denoising For denoising algorithms, even the noise standard deviation is perfectly estimated, they still cannot achieve the optimal PSNR or SSIM |
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Noise parameter estimation for signal-dependent noise Noisy image from real camera sensor is better modeled as the generalized signal-dependent noise. |
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Signal-dependent noise (Real sensor noise) removal A practical and high-quality signal-dependent noise (real sensor noise) algorithm is proposed in this research. |