l1 Regularized Super-resolution From Unregistered Omnidirectional Images

Zafer Arican, Pascal Frossard
2009
Article de conférence

In this paper, we address the problemof super-resolution from multiple low-resolution omnidirectional images with inexact registration. Such a problem is typically encountered in omnidirectional vision scenarios with reduced resolution sensors in imperfect settings. Several spherical images with arbitrary rotations in the SO(3) rotation group are used for the reconstruction of higher resolution images. We propose an l1 regularized total least squares normminimization method for joint registration and reconstruction with better stabilization and denoising. Experimental results show that regularization offers a quality improvement of up to 1dB. In addition, it reduces the number of low resolution images that are necessary to reconstruct a high resolution image at a target quality.

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Zafer Arican, Pascal Frossard
2009
Article de conférence

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https://infoscience.epfl.ch/record/130527?ln=fr

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Image resolution

Image resolution is the level of detail an holds. The term applies to digital images, film images, and other types of images. "Higher resolution" means more image detail. Image resolution can be meas

Méthode expérimentale

Les méthodes expérimentales scientifiques consistent à tester la validité d'une hypothèse, en reproduisant un phénomène (souvent en laboratoire) et en faisant varier un paramètre. Le paramètre que l

Super-résolution

En traitement du signal et en , la super-résolution désigne le processus qui consiste à améliorer la résolution spatiale, c'est-à-dire le niveau de détail, d'une image ou d'un système d'acquisition. C

Super-resolution from unregistered omnidirectional images

Zafer Arican, Pascal Frossard

This paper addresses the problem of super- resolution from low resolution spherical images that are not perfectly registered. Such a problem is typically en- countered in omnidirectional vision scenarios with re- duced resolution sensors in imperfect settings. Several spherical images with arbitrary rotations in the SO(3) rotation group are used for the reconstruction of higher resolution images. We first describe the impact of the registration error on the Spherical Fourier Transform coefficients. Then, we formulate the joint registration and reconstruction problem as a least squares norm minimization problem in the transform domain. Exper- imental results show that the proposed scheme leads to effective approximations of the high resolution images, even with large registration errors. The quality of the reconstructed images also increases rapidly with the number of low resolution images, which demonstrates the benefits of the proposed solution in super-resolution schemes.

2008

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Joint registration and super-resolution with omnidirectional images

Zafer Arican, Pascal Frossard

This paper addresses the reconstruction of high resolution omnidirectional images from multiple low resolution images with inexact registration. When omnidirectional images from low resolution vision sensors can be uniquely mapped on the 2-sphere, such a reconstruction can be described as a transform domain super-resolution problem in the spherical imaging framework. We describe how several spherical images with arbitrary rotations in the SO(3) rotation group contribute to the reconstruction of a high resolution image with help of the Spherical Fourier Transform (SFT). As low resolution images might not be perfectly registered in practice, the impact of inaccurate alignment on the transform coefficients is further analyzed. We then cast the joint registration and super- resolution problem as a total least squares norm minimization problem in the SFT domain. A l1- regularized total least squares problem is also considered. The regularized problem is solved efficiently by interior point methods. Experiments with synthetic and natural images show that the proposed scheme leads to effective reconstruction of high resolution images even when large registration errors exist in the low resolution images. The quality of the reconstructed images also increases rapidly with the number of low resolution images, which demonstrates the benefits of the proposed solution in super-resolution schemes. Finally, we highlight the benefit of the additional regularization constraint that clearly leads to reduced noise and improved reconstruction quality.

Institute of Electrical and Electronics Engineers2011

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Joint registration and super-resolution with omnidirectional images

Zafer Arican, Pascal Frossard

This paper addresses the reconstruction of high resolution omnidirectional images from multiple low resolution images with inexact registration. When omnidirectional images from low resolution vision sensors can be uniquely mapped on the 2-sphere, such a reconstruction can be described as a transform domain super-resolution problem in the spherical imaging framework. We describe how several spherical images with arbitrary rotations in the SO(3) rotation group contribute to the reconstruction of a high resolution image with help of the Spherical Fourier Transform (SFT). As low resolution images might not be perfectly registered in practice, the impact of inaccurate alignment on the transform coefficients is further analyzed. We then cast the joint registration and super-resolution problem as a total least squares norm minimization problem in the SFT domain. A l1- regularized total least squares problem is also considered. The regularized problem is solved efficiently by interior point methods. Experiments with synthetic and natural images show that the proposed scheme leads to effective reconstruction of high resolution images even when large registration errors exist in the low resolution images. The quality of the reconstructed images also increases rapidly with the number of low resolution images, which demonstrates the benefits of the proposed solution in super-resolution schemes. Finally, we highlight the benefit of the additional regularization constraint that clearly leads to reduced noise and improved reconstruction quality.

2009

Chargement