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The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.

State-of-the-art image denoising algorithms usually assume additive white Gaussian noise (AWGN), although they have achieved outstanding performance, modeling and removing real signal dependent noise from a single image still remains a challenging problem. In this paper we propose a segmentation-based image denoising algorithm for signal dependent noise. Incorporating a noise identification algorithm, we integrate these two modules into a full blind, end-to-end denoising algorithm for signal dependent noise. First, we identify the noise level function for a given single noisy image. Then, after initial denoising, segmentation is applied to the pre-filtered image. Assuming the noise level of each segment is constant, we apply AWGN denoising algorithm to each segment. We obtain a final de-noised image by composing the denoised segments. Various experimental results on synthetic and real noisy images show that our algorithm outperforms state-of-the-art denoising algorithms in removing real signal dependent noise.