Optimization of wavelet- and curvelet-based denoising algorithms by multivariate SURE and GCV

Denoising by Lossy Compression and Curvelet Thresholding

Curvelet Transform and Hybrid Bacterial Foraging Optimization for Image Denoising

Eliminating noise from the original image is still a challenging task for researchers. Several algorithms have been proposed and each of them has its own assumptions, advantages & limitations. The paper proposes the noise reduction method for the medical images by using Hybrid BFO i.e the fusion of BFO (Bacteria foraging optimization) and the technique of contourlet transform and the results ar...

Wavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting

In this paprer, a multivariate polynomial (MP) combined with denoising techniques is proposed to forecast 1-month ahead monthly anchovy catches in the north area of Chile. The anchovy catches data is denoised by using discrete stationary wavelet transform and then appropriate is used as inputs to the MP. The MP’s parameters are estimated using the penalized least square (LS) method and the perf...

CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet

One of the most important challenging factors in medical images is nominated as noise. Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjects to ...

Comparison and Assessment of Various Wavelet and Wavelet Packet based Denoising Algorithms for Noisy Data

Denoising of measured data is an important method in data analysis and of great significance in many industrial applications. For example pattern recognition often needs signal denoising as a kind of preprocessing, followed by the actual classification. Denoising of measured data can be seen as a problem in nonparametric regression where an unknown function f has to be revealed from a signal f̃ ...