Introduction: Clinicians use several computer-aided diagnostic systems for depression to authorize their diagnosis. An electroencephalogram  (EEG) may be used as an objective tool for early diagnosis of depression and controlling it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a no...

Denoising is a crucial preprocessing procedure for three dimensional magnetic resonance imaging (3D MRI). Existing denoising methods are predominantly implemented in a single domain, ignoring information in other domains. However, denoising methods are becoming increasingly complex, making analysis and implementation challenging. The present study aimed to develop a dual-domain image denoising ...

New imaging capabilities have given rise to higher dimensional image processing. This paper presents a generalization of total variation (TV) based denoising with specific application to three-dimensional flash lidar imagery. The generalization uses a weighted norm, rather than the standard Euclidian measure, that accounts for sampling differences that may exist along different axes. We compare...

Different methods for image denoising have complementary strengths and can be combined to improve image denoising performance, as has been noted by several authors [11, 7]. Mosseri et al. [11] distinguish between internal and external methods depending whether they exploit internal or external statistics [13]. They also propose a rule-based scheme (PatchSNR) to combine these two classes of algo...

Wavelet shrinkage denoising methods are widely used for estimation of biological signals from noisy environment. The popular Hard and Soft thresholding filters are commonly used in these methods. In this paper shrinkage method based on a New Thresholding filter for denoising of biological signals is proposed. The efficacy of this filter is evaluated by applying this filter for denoising of ECG ...

In this lecture we survey advanced denoising techniques. Unlike the Wiener filter which is an optimal linear estimator given the second order statistics of the noise and signal, the methods we consider in this lecture are non-linear and relies on a different prior, the sparsity one. Though it is used in many image processing problems, for the simplicity of the discussion we consider here only t...