Super-Resolved Surface Reconstruction from Multiple Images

Cheeseman, Peter; Kanefsky, Bob; Kraft, Richard; Stutz, John; Hanson, Robin

doi:10.1007/978-94-015-8729-7_23

Cited by 139 publications

(115 citation statements)

References 3 publications

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“…In 2D, the new fusion method aims at bringing new solutions to image mosaicing, co-addition, and super-resolution with an unprecedented management of uncertainties and an explicit handling of geometry, blur and noise statistics. Application areas include astronomy [4] where huge amounts of data are available within virtual observatories, as well as remote sensing and planetary imaging [3]. A common issue, particularly well-suited to the new fusion framework, is the optimal combination of images taken at different resolutions, in various viewing conditions (and not perfectly registered), with specific noise properties and possibly missing data.…”

Section: Possible Applications and Extensionsmentioning

confidence: 99%

Multisource data fusion for bandlimited signals: a Bayesian perspective

Jalobeanu¹,

Gutiérrez

2006

AIP Conference Proceedings

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Abstract. We consider data fusion as the reconstruction of a single model from multiple data sources. The model is to be inferred from a number of blurred and noisy observations, possibly from different sensors under various conditions. It is all about recovering a compound object, signal+uncertainties, that best relates to the observations and contains all the useful information from the initial data set.We wish to provide a flexible framework for bandlimited signal reconstruction from multiple data. In this paper, we focus on a general approach involving forward modeling (prior model, data acquisition) and Bayesian inference. The proposed method is valid for n-D objects (signals, images or volumes) with multidimensional spatial elements. For the sake of clarity, both formalism and test results will be shown in 1D for single band signals. The main originality lies in seeking an object with a prescribed bandwidth, hence our choice of a B-Spline representation. This ensures an optimal sampling in both signal and frequency spaces, and allows for a shift invariant processing.The model resolution, the geometric distortions, the blur and the regularity of the sampling grid can be arbitrary for each sensor. The method is designed to handle realistic Gauss+Poisson noise.We obtained promising results in reconstructing a super-resolved signal from two blurred and noisy shifted observations, using a Gaussian Markov chain as a prior. Practical applications are under development within the SpaceFusion project. For instance, in astronomical imaging, we aim at a sharp, well-sampled, noise-free and possibly super-resolved image. Virtual Observatories could benefit from such a way to combine large numbers of multispectral images from various sources. In planetary imaging or remote sensing, a 3D image formation model is needed; nevertheless, this can be addressed within the same framework.

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Section: Possible Applications and Extensionsmentioning

confidence: 99%

Multisource data fusion for bandlimited signals: a Bayesian perspective

Jalobeanu¹,

Gutiérrez

2006

AIP Conference Proceedings

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“…To our knowledge, a lot of well known and popular existing HR reconstruction methods are often based on a regularized approach [5,6,7]. In this work, which is an extension of our previous work [1], we use a Bayesian estimation framework which gives the possibility to account for a broad range of prior models to formulate inverse problem of multi-frame SR restoration (we can mention for instance the previous works [8,9]). The innovation of the present study lies in the definition of region's homogeneity which follows a bilinear model here, instead of a constant one in [1].…”

Section: Introductionmentioning

confidence: 99%

“…The Bayesian framework with the FMM is a common tool for classification but usually, the discrete variable associated to each pixel is supposed to be i.i.d. [8]. Our Potts Markov Model (PMM) for these variables allows to consider the spatial correlation of the pixels.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution and Joint Segmentation in Bayesian Framework

Humblot¹

2005

AIP Conference Proceedings

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Abstract.This communication presents an extension to a super-resolution (SR) method we previously exposed in [1]. SR techniques involve several low-resolution (LR) images in the reconstruction's process of a high-resolution (HR) image. The LR images are assumed to be obtained from the HR image through optical and sensor blurs, shift movement and decimation operators, and finally corruption by a random noise. Moreover, the HR image is assumed to be composed of a finite number of homogeneous regions. Thus, we associate to each pixel of the HR image a classification variable which is modeled by a Potts Markov field. The SR problem is then expressed as a Bayesian joint estimation of the HR image pixel values, its classification labels variable, and the problem's hyperparameters. These estimations are performed using an appropriate algorithm based on hybrid Markov Chain Monte-Carlo (MCMC) Gibbs sampling.In this study, we distinguish two kinds of region's homogeneity: the first one follows a constant model, and the second a bilinear model. Our previous work [1] only deals with constant model. Finally we conclude this work showing simulation results obtained with synthetic and real data.

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“…They derive the Cramér-Rao lower bound for the covariance of the error in the estimate of the super-resolved image and show that the estimate becomes better as the relative blur between the observations increases. Cheeseman et al [20] use a Bayesian method for constructing a super-resolved surface model by combining information from a set of images of the given surface. Their reconstruction gives the "emmitance" of the surface, which is a combination of the effects of surface albedo, illumination conditions, and ground slope for landsat images.…”

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confidence: 99%

“…The ML estimates of the SAR model parameters are obtained using the iterative estimation scheme as the loglikelihood function is nonquadratic. Although we use the MAP-MRF approach for super-resolution, our work is fundamentally different from that of [16], [20], and [36] in the sense that we learn the field parameters on the fly, whereas the previous works assume them to be known. Further, all previous methods use observations at the same resolution.…”

mentioning

confidence: 99%

A Learning-Based Method for Image Super-Resolution From Zoomed Observations

Joshi¹,

Chaudhuri

Panuganti

2005

IEEE Trans. Syst., Man, Cybern. B

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Abstract-We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. The high-resolution image is modeled through appropriate parameterization, and the parameters are learned from the most zoomed observation. Assuming a homogeneity of the high-resolution field, the learned model is used as a prior while super-resolving the scene. We suggest the use of either a Markov random field (MRF) or an simultaneous autoregressive (SAR) model to parameterize the field based on the computation one can afford. We substantiate the suitability of the proposed method through a large number of experimentations on both simulated and real data.

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Super-Resolved Surface Reconstruction from Multiple Images

Cited by 139 publications

References 3 publications

Multisource data fusion for bandlimited signals: a Bayesian perspective

Multisource data fusion for bandlimited signals: a Bayesian perspective

Super-Resolution and Joint Segmentation in Bayesian Framework

A Learning-Based Method for Image Super-Resolution From Zoomed Observations

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