Image Noise Level Estimation by Principal Component Analysis

By Stanislav Pyatykh, Jürgen W. Hesser, Lei Zheng. IEEE Transactions on Image Processing, 2013

346 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5a76dd3b4721d8cc4acb3d6332459d511b394245

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Lei Zhang
2. Wangmeng Zuo
3. Kai Zhang
4. Luc Van Gool
5. Radu Timofte
6. Ming-Hsuan Yang
7. Chen Change Loy
8. Chunhua Shen

In Media Technology

1. Lei Zhang
2. Kai Zhang
3. Wangmeng Zuo
4. Radu Timofte
5. Luc Van Gool
6. Shi Guo
7. Stamatios Lefkimmiatis
8. Masatoshi Okutomi

In Signal Processing

1. Kai Zhang
2. Wangmeng Zuo
3. Masatoshi Okutomi
4. Lei Zhang

Citations in the corpus

• Single-Image Noise Level Estimation for Blind Denoising (2013, background): “[7], in which a number of patches with largest variances are discarded.”
• Shape Completion from a Single RGBD Image (2017, background): “l is the voxel’s noise level estimated with [39], and l = 0 :05 is the mean noise level of Kinect data (estimated from 100 depth images).”
• Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition (2014, background): “There are numerous means to estimate noise properties from data, see Pyatykh et al. (2013), for example.”
• Toward Convolutional Blind Denoising of Real Photographs (2018, methodology): “For AWGN, several PCAbased [48, 34, 9] methods have been developed for estimating noise standard deviation (SD.”