Fundamentals of digital image processing
Fundamentals of digital image processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Multirate systems and filter banks
Multirate systems and filter banks
Speckle suppression in ultrasonic images based on undecimated wavelets
EURASIP Journal on Applied Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Multiscale MAP filtering of SAR images
IEEE Transactions on Image Processing
Filtering in Generalized Signal-Dependent Noise Model Using Covariance Information
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Simplified noise model parameter estimation for signal-dependent noise
Signal Processing
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This paper addresses the topic of filtering digital images corrupted by signal-dependent additive white noise. The noise model is fully parametric to take into account different noise generation processes, like speckle and film-grain noise. Noise reduction is first approached as a linear minimum mean square error estimation in the spatial domain, thus extending previous results to the most general signal-dependent white noise model. The same type of estimation is performed in a shift-invariant wavelet domain, in which the absence of decimation of the decomposition avoids the typical ringing/aliasing impairments of critically subsampled wavelet-based denoising schemes. In the former case, filtered pixel values are obtained as adaptive combinations of raw and of local average values, driven by locally computed statistics. In the latter case, detail wavelet coefficients of the noisy image are adaptively shrunk by using local statistics derived from the noisy image and the noise model, before the denoised image is synthesised. Experimental results demonstrate that the proposed approaches take full advantage of the knowledge of the underlying noise model. Furthermore, the multiresolution algorithm steadily outperforms the spatial counterpart in terms of both SNR increment and of enhancement in visual quality.