19 research outputs found
Multichannel Seismic Deconvolution Using Bayesian Method
In this paper, we propose an algorithm for multichannel blind deconvolution of seismic signals, which exploits variational Bayesian method. It is related to the Kullback-Leibler divergence, which measures the independence degree of deconvolved data sequence. We assume that the reflectivity sequence is almost the same for each receiver while the noise level may differ at each channel. Compared to blind deconvolution of a single seismic trace, multichannel blind deconvolution provides an accurate convergence of the estimated parameters and reflectivity sequence
Semi-blind Sparse Image Reconstruction with Application to MRFM
We propose a solution to the image deconvolution problem where the
convolution kernel or point spread function (PSF) is assumed to be only
partially known. Small perturbations generated from the model are exploited to
produce a few principal components explaining the PSF uncertainty in a high
dimensional space. Unlike recent developments on blind deconvolution of natural
images, we assume the image is sparse in the pixel basis, a natural sparsity
arising in magnetic resonance force microscopy (MRFM). Our approach adopts a
Bayesian Metropolis-within-Gibbs sampling framework. The performance of our
Bayesian semi-blind algorithm for sparse images is superior to previously
proposed semi-blind algorithms such as the alternating minimization (AM)
algorithm and blind algorithms developed for natural images. We illustrate our
myopic algorithm on real MRFM tobacco virus data.Comment: This work has been submitted to the IEEE Trans. Image Processing for
possible publicatio
Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
As a powerful statistical image modeling technique, sparse representation has
been successfully used in various image restoration applications. The success
of sparse representation owes to the development of l1-norm optimization
techniques, and the fact that natural images are intrinsically sparse in some
domain. The image restoration quality largely depends on whether the employed
sparse domain can represent well the underlying image. Considering that the
contents can vary significantly across different images or different patches in
a single image, we propose to learn various sets of bases from a pre-collected
dataset of example image patches, and then for a given patch to be processed,
one set of bases are adaptively selected to characterize the local sparse
domain. We further introduce two adaptive regularization terms into the sparse
representation framework. First, a set of autoregressive (AR) models are
learned from the dataset of example image patches. The best fitted AR models to
a given patch are adaptively selected to regularize the image local structures.
Second, the image non-local self-similarity is introduced as another
regularization term. In addition, the sparsity regularization parameter is
adaptively estimated for better image restoration performance. Extensive
experiments on image deblurring and super-resolution validate that by using
adaptive sparse domain selection and adaptive regularization, the proposed
method achieves much better results than many state-of-the-art algorithms in
terms of both PSNR and visual perception.Comment: 35 pages. This paper is under review in IEEE TI
A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes
QR bar codes are prototypical images for which part of the image is a priori
known (required patterns). Open source bar code readers, such as ZBar, are
readily available. We exploit both these facts to provide and assess purely
regularization-based methods for blind deblurring of QR bar codes in the
presence of noise.Comment: 14 pages, 19 figures (with a total of 57 subfigures), 1 table; v3:
previously missing reference [35] adde