Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Space-scale adaptive noise reduction in images based on thresholding neural network
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Image denoising with neighbour dependency and customized wavelet and threshold
Pattern Recognition
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Image enhancement based on a nonlinear multiscale method
IEEE Transactions on Image Processing
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Thresholding neural network for adaptive noise reduction
IEEE Transactions on Neural Networks
Fast adaptive learning algorithm for sub-band adaptive thresholding function in image denoising
International Journal of Computational Intelligence Studies
Journal of Mathematical Imaging and Vision
VLSI-DSP based real time solution of DSC-SRI for an ultrasound system
Microprocessors & Microsystems
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In this paper, a new thresholding function is proposed for image denoising in the wavelet domain. The proposed function is further used in a new subband-adaptive thresholding neural network to improve the efficiency of the denoising procedure. Some new adaptive learning types are also proposed. In these learning methods, the threshold and the thresholding function effects are considered simultaneously. These methods are used to suppress two types of important noises, Gaussian and speckle, ranging from natural images to ultrasound and SAR pictures. The simulation results show that the proposed thresholding function has superior features compared to conventional methods when used with the proposed adaptive learning types. This makes it an efficient method in image denoising applications.