Denoising scheme for realistic digital photos from unknown sources

Lim, Suk Hwan; Maurer, Ron; Kisilev, Pavel

doi:10.1109/icassp.2009.4959802

Cited by 4 publications

(3 citation statements)

References 14 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, homogeneous noise assumption is not valid in real-world images [8]. Figure 1 illustrates a linear RGB image without denoising or gamma correction.…”

Section: Introductionmentioning

confidence: 99%

“…Given a noisy image, the proposed method is able to produce a pixelwise noise-level estimation map as well as a global scalar noise level. The contributions of this study are as follows:Although 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. The method works on a more precise signal-dependent noise model (1).…”

Section: Introductionmentioning

confidence: 99%

“…Although 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. The method works on a more precise signal-dependent noise model (1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning

Tan

Xiao

Lai

et al. 2019

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

“…However, homogeneous noise assumption is not valid in real-world images [8]. Figure 1 illustrates a linear RGB image without denoising or gamma correction.…”

Section: Introductionmentioning

confidence: 99%