Image denoising based on a probabilistic model of local image patches has been employed by various researchers, and recently a deep (denoising) autoencoder has been proposed by Burger et al. [2012] and Xie et al. [2012] as a good model for this. In this paper, we propose that another popular family of models in the field of deep learning, called Boltzmann machines, can perform image denoising a...

We propose dual-domain filtering, an image processing paradigm that couples spatial domain with frequency domain filtering. Our dual-domain defined filter removes artifacts like residual noise of other image denoising methods and compression artifacts. Moreover, iterating the filter achieves state-of-the-art image denoising results, but with a much simpler algorithm than competing approaches. T...

This paper addresses two problems: an image denoising problem assuming dense observations and an image reconstruction problem from sparse data. It shows that both problems can be solved by the Sylvester/Lyapunov algebraic equation. The Sylvester/Lyapunov equation has been extensively studied in Control Theory and it can be efficiently solved by well known numeric algorithms. This paper proposes...

The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and creat...

MOST existing digital color cameras use a single sensor with a color filter array (CFA) to capture visual scenes in color. Since each sensor cell can record only one color value, the other two missing color components at each position need to be interpolated. The color interpolation process is usually called color demosaicking (CDM). The quality of demosaicked images is degraded due to the sens...

Computed tomography (CT) has a revolutionized diagnostic radiology but involves large radiation doses that directly impact image quality. In this paper, we propose adaptive tensor-based principal component analysis (AT-PCA) algorithm for low-dose CT image denoising. Pixels in the image are presented by their nearby neighbors, and are modeled as a patch. Adaptive searching windows are calculated...

—We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g. blocks) into 3D data arrays which we call "groups". Collaborative ltering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformat...

A dual tree complex wavelet transform (DT -CWT)based directional interpolation scheme for denoising of noisy images is proposed . The problems of denoising and interpolation are modeled as to estimate the noiseless and missing samples under the same framework of optimal estimation. Initially, DT -CWT is used to decompose an input low-resolution noisy wage irito low and high frequency subbands. ...

Introduction: In this study the evaluation of a Platelet-based Maximum Penalized Likelihood Estimation (MPLE) for denoising SPECT images was performed and compared with other denoising methods such as Wavelets or Butterworth filtration. Platelet-based MPLE factorization as a multiscale decomposition approach has been already proposed for better edges and surfaces representation due to Poi...

Statistical analysis has proven very successful in the image processing community. Linear methods such as principal component analysis (PCA) measure the degree of correlation in datasets to extract meaningful information from highdimensional data. PCA was successfully applied in several applications such as image segmentation with shape priors and image denoising. The major assumption in these ...