Denoising of multicomponent images using wavelet least-squares estimators
Image and Vision Computing
Feature space and metric measures for fusing multisensor images
International Journal of Remote Sensing
Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images
IEEE Transactions on Image Processing
A wavelet-based image denoising using least squares support vector machine
Engineering Applications of Artificial Intelligence
Multicomponent image restoration, an experimental study
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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In this paper, a denoising technique for multivalued images exploiting interband correlations is proposed. A redundant wavelet transform is applied and denoising is applied by thresholding wavelet coefficients. Specific functions of the wavelet coefficients are defined that exploit interscale and/or interband correlation of the signal. Three functions are studied: the square of the wavelet coefficients, products of coefficients at adjacent scales, and products of coefficients from different bands. For these functions, the signal and noise probability density functions (pdf) become more separated. The high signal correlation between bands is exploited by summing these products over all bands, in this way separating noise and signal pdfs even more. The noise pdf of the proposed quantities is derived analytically and from this, a wavelet threshold is derived. The technique is demonstrated to outperform single band wavelet thresholding on multispectral remote sensing images and on multimodal MRI images.