Fundamentals of digital image processing
Fundamentals of digital image processing
Image denoising via lossy compression and wavelet thresholding
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Wavelet filter for noise reduction and signal compression in an artificial nose
Applied Soft Computing
The landscape adaptive particle swarm optimizer
Applied Soft Computing
A robust neuro-fuzzy network approach to impulse noise filtering for color images
Applied Soft Computing
Bayesian sigmoid shrinkage with improper variance priors and an application to wavelet denoising
Computational Statistics & Data Analysis
A B-wavelet-based noise-reduction algorithm
IEEE Transactions on Signal Processing
Two denoising methods by wavelet transform
IEEE Transactions on Signal Processing
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Fuzzy-based learning rate determination for blind source separation
IEEE Transactions on Fuzzy Systems
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Wavelet thresholding for multiple noisy image copies
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Noise reduction without any prior knowledge of noise or signals is addressed in this study. Compared with conventional filters, wavelet shrinkage can respect this requirement to reduce noise from received signal in wavelet coefficients. However, wavelet threshold depends on an estimate of noise deviation and a weight relating signal's length cannot be applied in every case. This paper uses particle swarm optimization (PSO) to explore a suitable threshold in a complete solution space, named PSOShrink. A general-purpose objective function which is derived from blind signal separation (BSS) theory is further proposed. In simulation, four benchmarks signals and three degrading degrees are testing; meanwhile, three existing algorithm with state-of-the-art are performed for comparison. PSOShrink can not only recovers source signals from a heavy blurred signal but also remains details of a source signal from a light blurred signal; moreover, it performs outstanding denoising in every simulation case.