Two image denoising approaches based on wavelet neural network (WNN) optimized by particle swarm optimization (PSO) are proposed. The noisy image is filtered by the modified median filtering (MMF). Feature values are extracted based on the MMF and th
A new locally adaptive image denoising method, which exploits the intra-scale and inter-scale dependency in the dual-tree complex wavelet domain, is presented. Firstly, a recently emerged bivariate shrinkage rule is extended to a complex coefficient
We propose a new method for image denoising combining wavelet transform and support vector machines (SVMs). A new image filter operator based on the least squares wavelet support vector machines (LS-WSVMs) is presented. Noisy image can be denoised th
Generalized cross validation (GCV) is a significant mean square error (MSE) estimator. It is widely used for image denoising because it can provide an optimal denoising threshold for these wavelet coefficients of noise image. However, the computation
Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information
