A nonseparable VLSI architecture for two-dimensional discrete periodized wavelet transform
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
The application of Markov random field models to wavelet-based image denoising
Imaging and vision systems
Journal of Mathematical Imaging and Vision
Training methods for image noise level estimation on wavelet components
EURASIP Journal on Applied Signal Processing
Removal of correlated noise by modeling the signal of interest in the wavelet domain
IEEE Transactions on Image Processing
L1 prior majorization in Bayesian image restoration
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
A SURE approach for digital signal/image deconvolution problems
IEEE Transactions on Signal Processing
CIDER: corrected inverse-denoising filter for image restoration
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Image sharpening by DWT-based hysteresis
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
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In this paper, we present a new spatially adaptive approach to the restoration of noisy blurred images, which is particularly effective at producing sharp deconvolution while suppressing the noise in the flat regions of an image. This is accomplished through a multiscale Kalman smoothing filter applied to a prefiltered observed image in the discrete, separable, 2-D wavelet domain. The prefiltering step involves constrained least-squares filtering based on optimal choices for the regularization parameter. This leads to a reduction in the support of the required state vectors of the multiscale restoration filter in the wavelet domain and improvement in the computational efficiency of the multiscale filter. The proposed method has the benefit that the majority of the regularization, or noise suppression, of the restoration is accomplished by the efficient multiscale filtering of wavelet detail coefficients ordered on quadtrees. Not only does this lead to potential parallel implementation schemes, but it permits adaptivity to the local edge information in the image. In particular, this method changes filter parameters depending on scale, local signal-to-noise ratio (SNR), and orientation. Because the wavelet detail coefficients are a manifestation of the multiscale edge information in an image, this algorithm may be viewed as an “edge-adaptive” multiscale restoration approach