Blind image noise level estimation using texture-based eigenvalue analysis
- 351 Downloads
Blind noisy image estimation is useful in many visual processing systems. The challenge lies in accurately estimating the image noise level without any priori information of the image. To tackle this challenge, an iterative texture-based eigenvalue analysis approach is proposed in this paper. The proposed approach utilizes the eigenvalue analysis to mathematically derive a new noise level estimator based on weak-textured image patches. Furthermore, a new texture strength measure is proposed to adaptively select weak-textured patches from the noisy image. Experimental results are provided to demonstrate that the proposed image noise level estimation approach yields superior accuracy and stability performance to that of conventional noise level estimation approaches, so that to improve the performance of image denoising algorithm.
KeywordsNoise level estimation Eigenvalue analysis Image denoising
This work was supported by National Natural Science Foundation of China (No. 61105010, 61375017), Program for Outstanding Young Science and Technology Innovation Teams in Higher Education Institutions of Hubei Province, China (No. T201202).
- 3.Chen L, Huang X, Tian J, Fu X (2014) Blind noisy image quality evaluation using a deformable ant colony algorithm. Opt Laser Technol 57:265–270Google Scholar
- 5.Gai S, Luo L. (2013) Image denoising using normal inverse Gaussian model in quaternion wavelet domain, Multimedia Tools and Applications, pp 1–18, acceptedGoogle Scholar
- 7.Li B, Lin G, Chen Q, Wang H (2013) Image denoising with patch estimation and low patch-rank regularization, Multimedia Tools and Applications, acceptedGoogle Scholar
- 9.Lowe D (1999) Object recognition from local scale-invariant features. In: Proceedings IEEE International Conference on Computer Vision, Kerkyra, Greece, pp 1150–1157Google Scholar
- 10.Muresan D, Parks T (2003) Adaptive principal components and image denoising, In: Proceedings International Conference on Image Processing, Barcelona, Spain, pp 101–104Google Scholar
- 14.Tai S-C, Yang S-M (2008) A fast method for image noise estimation using Laplacian operator and adaptive edge detection, In: International Symposium on Communications, Control and Signal Processing, St. Julians, Malta, pp 1077–1081Google Scholar
- 19.Zeng W, Lu X, Tan X, Tan X (2013) A local structural adaptive partial differential equation for image denoising, Multimedia Tools and Applications, acceptedGoogle Scholar