Image Noise Level Estimation by Principal Component Analysis
Top Cited Papers
- 28 September 2012
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 22 (2), 687-699
- https://doi.org/10.1109/tip.2012.2221728
Abstract
The problem of blind noise level estimation arises in many image processing applications, such as denoising, compression, and segmentation. In this paper, we propose a new noise level estimation method on the basis of principal component analysis of image blocks. We show that the noise variance can be estimated as the smallest eigenvalue of the image block covariance matrix. Compared with 13 existing methods, the proposed approach shows a good compromise between speed and accuracy. It is at least 15 times faster than methods with similar accuracy, and it is at least two times more accurate than other methods. Our method does not assume the existence of homogeneous areas in the input image and, hence, can successfully process images containing only textures.Keywords
This publication has 48 references indexed in Scilit:
- Bayesian deblurring with integrated noise estimationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Learning a blind measure of perceptual image qualityPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Scale invariance and noise in natural imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Fields of ExpertsInternational Journal of Computer Vision, 2009
- A theoretical result for processing signals that have unknown distributions and priors in white Gaussian noiseComputational Statistics & Data Analysis, 2008
- Principal components for non-local means image denoisingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Assessing Noise Amplitude in Remotely Sensed Images Using Bit-Plane and Scatterplot ApproachesIEEE Transactions on Geoscience and Remote Sensing, 2007
- Developing Nonstationary Noise Estimation for Application in Edge and Corner DetectionIEEE Transactions on Image Processing, 2007
- Adaptive principal components and image denoisingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Noise estimation and filtering using block-based singular value decompositionIEEE Transactions on Image Processing, 1997