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.

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.

The additive white Gaussian noise (AWGN) is usually assumed in many image processing algorithms. However, these algorithms cannot effectively deal with the noise from actual cameras which is better modeled as signal dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to accurately estimate its parameters without any assumption of the noise types. The noise parameters are estimated by using the selected weak textured patches from a single noisy image. Experiments on synthetic noisy images are conducted to test the algorithm, which show that our noise parameter estimation outperforms the existing algorithms. And based on our estimation, the performance of image processing applications like Wiener filter can be effectively improved.

Noise level is an important parameter to many image processing applications. For example, the performance of an image denoising algorithm can be much degraded due to the poor noise level estimation. Most existing denoising algorithms simply assume the noise level is known that largely prevents them from practical use. Moreover, even with the given true noise level, these denoising algorithms still cannot achieve the best performance, especially for scenes with rich texture. In this paper, we propose a patch-based noise level estimation algorithm and suggest that the noise level parameter should be tuned according to the scene complexity. Our approach includes the process of selecting low-rank patches without high frequency components from a single noisy image. The selection is based on the gradients of the patches and their statistics. Then, the noise level is estimated from the selected patches using principal component analysis. Because the true noise level does not always provide the best performance for nonblind denoising algorithms, we further tune the noise level parameter for nonblind denoising. Experiments demonstrate that both the accuracy and stability are superior to the state of the art noise level estimation algorithm for various scenes and noise levels.

A patch-based noise level estimation algorithm is proposed in this paper, with patches generated from a single noisy image. One can easily estimate the noise level from image patches using principal component analysis (PCA) if the image comprises only weak textured patches. The challenge for patch-based noise level estimation is how to select weak textured patches from a noisy image. As described in this paper, we propose a novel algorithm to select weak textured patches from a single noisy image based on the gradients of the patches and their statistics. Then we estimate the noise level from the selected weak textured patches using PCA. We demonstrate experimentally that the proposed noise level estimation algorithm outperforms the state-of-the-art algorithm.