This thesis focuses on denoising of positron emission tomography (PET) data. Cardiac PET scans generated using a rubidium-82 radiotracer are a convenient, non– invasive method of diagnosing heart disease, but suffer from a high degree of noise. Denoising methods based on the wavelet transform are capable of outperforming existing clinical methods due to their ability to better preserve detail w...

Mesh denoising is a fundamental problem in digital geometry processing. It seeks to remove surface noise while preserving intrinsic signals as accurately possible. While traditional wisdom has been built upon specialized priors smooth surfaces, learning-based approaches are making their debut with great success generalization and automation. In this work, we provide comprehensive review of the ...

State-of-the-art denoising methods achieve impressive results, even for large noise levels. However, they can not be implemented in camera hardware, mainly due to the fact that they are computationally too intensive. The aim of this paper is then to show that we can obtain comparable denoising results to the ones obtained with state-of-art methods by inserting a well-chosen fast denoising metho...

We consider the determination of a soft/hard coefficients threshold for signal recovery embedded in additive Gaussian noise. This is closely related to the problem of variable selection in linear regression. Viewing the denoising problem as a model selection one, we propose a new information theoretical model selection approach to signal denoising. We first construct a statistical model for the...

In this paper, we study denoising of multicomponent images. The presented procedures are spatial wavelet-based denoising techniques, based on Bayesian leastsquares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the spectral bands. We analyze three mixture priors: Gaussian scale mixture models, Bernoulli-Gaussian mixture models ...

A modified spatially adaptive denoising algorithm for a single image corrupted by Gaussian noise is proposed in this paper. The proposed algorithm use local statistics of a selected window i.e. by defining local weighted mean, local weighted activity and local maximum. These local statistics are used to detect the noise in the image then a modified Gaussian filter is used for noise suppression....

With the growing popularity of smartphones, capturing high-quality images is vital importance to smartphones. The cameras smartphones have small apertures and sensor cells, which lead noisy in low light environment. Denoising based on a burst multiple frames generally outperforms single frame denoising but with larger compututional cost. In this paper, we propose an efficient yet effective syst...