Abstract
In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, real-world noise is signal dependent, or the noise level is not constant over the whole image. In this paper, we attempt to estimate the precise and pixelwise noise level instead of a simple global scalar. To the best of our knowledge, this is the first work on the problem. Particularly, we propose a deep convolutional neural network named “deep residual noise estimator” (DRNE) for pixelwise noise-level estimation. We carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation. The proposed DRNE formulates the process of noise estimation as pixel-to-pixel prediction. The experimental results show that the DRNE can achieve better performance on nonhomogeneous noise estimation than state-of-the-art methods. In addition, the DRNE can bring denoising performance gains in removing signal-dependent Gaussian noise when working with recent deep learning denoising methods.
Highlights
Noise level is an important parameter which decides how much the input noisy image should be smoothed. is parameter is directly required for many well-known denoising algorithms including Wiener filtering [1], nonlocal means (NLM) [2], BM3D [3], sparse representation-based denoising [4], and deep learning denoising methods [5, 6].Image denoising is often formulated to remove Gaussian white noise, which is additive and homogeneous
Homogeneous means that the noise variance is a constant for all pixels over the input noisy image and does not change over the position or color intensity of a pixel [7]. is formulation significantly simplifies the process of image denoising, where noise level is the only parameter required to model noise
We propose a deep residual convolutional neural network for pixelwise noise-level estimation. e main architecture consists of a stack of customized residual blocks, which is more proper for noise estimation
Summary
Noise level is an important parameter which decides how much the input noisy image should be smoothed. is parameter is directly required for many well-known denoising algorithms including Wiener filtering [1], nonlocal means (NLM) [2], BM3D [3], sparse representation-based denoising [4], and deep learning denoising methods [5, 6]. We propose a deep residual convolutional neural network for pixelwise noise-level estimation. No pooling operation or a larger stride of convolution is adopted in the proposed architecture, which always intends to extract high-level features, such as semantic information Such a refined feature is unnecessary for noise estimation, which always focuses on low-level features, e.g., boundary and local variance. The proposed method is able to produce a pixelwise noise-level estimation map as well as a global scalar noise level. (1) many works have pointed out that noise standard deviation is not uniform across the image [8, 9], this paper is the first work to give a pixelwise Gaussian noise-level estimation map. (3) In terms of traditional scalar average estimation error, the proposed method is able to compete with the state-of-the-art methods [7] in both traditional homogeneous scalar noise estimation and nonhomogeneous noise estimation
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