96,472 research outputs found
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
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
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