Journal ArticleDOI
Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images
TLDR
This work proposes two algorithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images based on a Bayesian formulation that is implemented via the expectation maximization algorithm and a maximum a posteriori formulation.Abstract:
Using a stochastic framework, we propose two algorithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images. The first is based on a Bayesian formulation that is implemented via the expectation maximization algorithm. The second is based on a maximum a posteriori formulation. In both of our formulations, the registration, noise, and image statistics are treated as unknown parameters. These unknown parameters and the high-resolution image are estimated jointly based on the available observations. We present an efficient implementation of these algorithms in the frequency domain that allows their application to large images. Simulations are presented that test and compare the proposed algorithms.read more
Citations
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Journal ArticleDOI
Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution
TL;DR: A sub-pixel shifting based super-resolution algorithm is implemented to effectively recover much higher resolution digital holograms of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area.
Journal ArticleDOI
Super-Resolution Without Explicit Subpixel Motion Estimation
TL;DR: This paper introduces a novel framework for adaptive enhancement and spatiotemporal upscaling of videos containing complex activities without explicit need for accurate motion estimation based on multidimensional kernel regression, which significantly widens the applicability of super-resolution methods to a broad variety of video sequences containing complex motions.
Journal ArticleDOI
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
TL;DR: This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-sp spatial high-spectrals resolution image, and exploits the properties of the circulant and downsampling matrices associated with the fusion problem.
Journal ArticleDOI
Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI
TL;DR: A novel technique based on a slice acquisition model, which enables the reconstruction of a volumetric image from multiple-scan slice acquisitions and a robust M-estimation solution which minimizes a robust error norm function between the model-generated slices and the acquired slices are developed.
Journal ArticleDOI
A super-resolution reconstruction algorithm for surveillance images
TL;DR: An edge-preserving maximum a posteriori (MAP) estimation based super-resolution algorithm using a weighted directional Markov image prior model for a ROI from more than one low-resolution surveillance image is proposed.
References
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Maximum likelihood from incomplete data via the EM algorithm
Numerical recipes in C
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
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Super-resolution image reconstruction: a technical overview
TL;DR: The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts to present the technical review of various existing SR methodologies which are often employed.
Book ChapterDOI
A view of the EM algorithm that justifies incremental, sparse, and other variants
TL;DR: In this paper, an incremental variant of the EM algorithm is proposed, in which the distribution for only one of the unobserved variables is recalculated in each E step, which is shown empirically to give faster convergence in a mixture estimation problem.