An algorithm with L1 and L2 mixed norm and bilateral total variation(BTV) regularization is proposed in this paper for image super-resolution. First, the mixed norm is used as the constraint of image fidelity; Secondly, considering the effect of the BTV method is not ideal for reconstruction in the edge and texture region, an adaptive regularization parameter algorithm is proposed. In the propo...

In many imaging applications, there exists potential for corruption of the images by sources of structured noise that completely loses original pixel information. The reconstruction of the original image from its corrupted observation is known as image inpainting. This paper seeks to investigate image inpainting using a particular algorithm, the alternating direction method of multipliers (ADMM...

Over the last few years, a growing number of researchers from varied disciplines have been utilizing Markov random fields (MRF) models for developing optimal, robust algorithms for various problems, such as texture analysis, image synthesis, image restoration, classification and segmentation, surface reconstruction, integration of several low level vision modules and sensor fusion. While linear...

introduction:  the  purpose  of  this  study  is  to  define  the  optimal  parameters  for  the  tomographic  reconstruction procedure in a routine single photon emission tomography. the hoffman brain phantom  is modified to evaluate the reconstruction method. the phantom was imaged in a 3 and 2-dimensional  conformation and the results were compared.   materials  and  methods:  the  2d  phant...

Abstract We reconstruct the history of reionization using Gaussian process regression. Using UV luminosity data compilation from Hubble Frontiers Fields we redshift evolution density and thereby source term in ionization equation. This model-independent reconstruction rules out single power-law but supports logarithmic double parameterization. obtain by integrating equations with reconstructed ...

Reconstruction error bounds in compressed sensing under Gaussian or uniform bounded noise do not translate easily to the case of Poisson noise. Reasons for this include the signal dependent nature of Poisson noise, and also the fact that the negative log likelihood in case of a Poisson distribution (which is directly related to the generalized Kullback-Leibler divergence) is not a metric and do...

We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM). While the compressive sensing is performed globally on the entire image as implemented in our lensless camera, a lowrank GMM is imposed on the local image patches. This lowrank GMM is derived via eigenvalue thresholding of the GMM trained on the projection of the measurement data, thus l...

This study examines the use of Gaussian process (GP) regression for sound field reconstruction. GPs enable reconstruction a from limited set observations based on covariance function (a kernel) that models spatial correlation between points in field. Significantly, approach makes it possible to quantify uncertainty closed form. In this study, relation and classical methods linear is examined an...