%K Artificial intelligence; Signal analysis; Speckle; Synthetic aperture radar, Anisotropic diffusion filters; Blurring effect; Image distortions; Noise subspace; Noisy image; Residual noise; SAR Images; Signal sub-space; Signaltonoise ratio (SNR); Simulated images; Speckle noise; Speckle noise reduction; Spectral components, Signal to noise ratio
%X In this paper, a subspace-based technique for speckle noise reduction in images is proposed. The method is based on a linear model obtained by minimizing the energy of image distortion while keeping the energy of the residual noise in each spectral component below some given threshold. Image enhancement is achieved by removing the noise subspace and estimating the clean signal from the remaining signal subspace. The performance of the proposed approach is tested with simulated images and real SAR images, and compared with Lee and homomorphic anisotropic diffusion filters. The results indicate that the proposed technique increases the signal-to-noise ratio (SNR) by 3.6dB to 5.7dB over the noisy image. In addition, the proposed SDC algorithm is better at preserving the fine texture and introduces less blurring effect into the denoised image. © 2012 IEEE.
%R 10.1109/CyberneticsCom.2012.6381640
%D 2012
%J Proceeding - 2012 IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012
%L scholars3140
%O cited By 0; Conference of 2012 1st IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012 ; Conference Date: 12 July 2012 Through 14 July 2012; Conference Code:95862
%C Bali
%I IEEE Computer Society
%A N. Yahya
%A N.S. Kamel
%A A.S. Malik
%T Speckle noise filtering based on signal subspace technique
%P 168-174