Directional ℓ0 Sparse Modeling for Image Stripe Noise Removal

L0 sparse graphical modeling

Graphical models are well established in providing compact conditional probability descriptions of complex multivariable interactions. In the Gaussian case, graphical models are determined by zeros in the precision or concentration matrix, i.e. the inverse of the covariance matrix. Hence, there has been much recent interest in sparse precision matrices in areas such as statistics, machine learn...

PATIL, RAJWADE: POISSON NOISE REMOVAL FOR IMAGE DEMOSAICING 1 Poisson Noise Removal for Image Demosaicing

With increasing resolution of the sensors in camera detector arrays, acquired images are ever more susceptible to perturbations that appear as grainy artifacts called ‘noise’. In real acquisitions, the dominant noise model has been shown to follow the Poisson distribution, which is signal dependent. Most color image cameras today acquire only one out of the R, G, B values per pixel by means of ...

Poisson Noise Removal for Image Demosaicing

Most color image cameras today acquire only one out of the R, G, B values per pixel by means of a color filter array (CFA) in the hardware producing the so called ‘CFA image’. In-built software routines are required to undertake the task of obtaining the rest of the color information at each pixel through a process termed demosaicing. The most common CFA pattern is the well-known Bayer pattern ...

Directional Total Generalized Variation Regularization for Impulse Noise Removal

Enhanced Cellular Automata for Image Noise Removal

Cellular Automata (CA) are a type of complex systems based on simple and uniformly interconnected cells. They provide an excellent method to perform complex computations in a simple way. CA can be used in image processing, because of the simplicity of mapping a digital image to a cellular automata and the ability of applying different image processing operations in real time. Noise removal is c...