A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
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
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Thresholding neural network for adaptive noise reduction
IEEE Transactions on Neural Networks
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The speed of image denoising by adaptive thresholding approach in Wavelet Transform (WT) domain depends mainly upon the learning algorithm used for optimising the performance of adaptive thresholding function. In this context, in the literature, steepest gradient-based optimisation technique has been used in WT-based thresholding neural network (WT-TNN) approach, which has low learning speed. In this paper, a new computationally efficient approach, that is, Particle Swarm Optimisation (PSO)-based approach has been proposed in place of steepest gradient-based approach. The proposed hybrid computing approach utilises the features of WT-TNN approach and enhances the speed of optimisation by PSO technique. It also yields better performance of denoising as compared to WT-TNN approach. In the proposed approach, crucial problem of initialisation of thresholding parameters gets automatically sorted out besides learning time becoming independent of noise level of the image. The proposed approach also enhances edge preservation, when implemented with bior6.8 wavelet filters.