Title :
Complexity-regularized image denoising
Author :
Liu, Juan ; Moulin, Pierre
Author_Institution :
Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA
Abstract :
We introduce a new complexity regularization method for image denoising and explore the use of sophisticated complexity penalties. We have found improvements of the order of 2 dB in reconstructed image mean-squared error over existing complexity-regularized estimators
Keywords :
Gaussian noise; computational complexity; image reconstruction; maximum likelihood estimation; white noise; AWGN; complexity penalties; complexity-regularized estimators; complexity-regularized image denoising; maximum likelihood estimation; reconstructed image mean-squared error; AWGN; Additive noise; Additive white noise; Bayesian methods; Gaussian noise; Image denoising; Image reconstruction; Maximum likelihood estimation; Noise reduction; Pixel;
Conference_Titel :
Image Processing, 1997. Proceedings., International Conference on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
0-8186-8183-7
DOI :
10.1109/ICIP.1997.638778
