Adaptive Outlier Rejection in Image Super-resolution
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Adaptive Outlier Rejection in Image Super-resolution 2 ¨ ainen ¨ Mejdi Trimeche,1 Radu Ciprian Bilcu,1 and Jukka Yrjan 1 Multimedia 2 Symbian
Technologies Laboratory, Nokia Research Center, Visiokatu 1, 33720 Tampere, Finland Product Platforms, Nokia Technology Platforms, Hermiankatu 12, 33720 Tampere, Finland
Received 29 November 2004; Revised 10 May 2005; Accepted 27 May 2005 One critical aspect to achieve efficient implementations of image super-resolution is the need for accurate subpixel registration of the input images. The overall performance of super-resolution algorithms is particularly degraded in the presence of persistent outliers, for which registration has failed. To enhance the robustness of processing against this problem, we propose in this paper an integrated adaptive filtering method to reject the outlier image regions. In the process of combining the gradient images due to each low-resolution image, we use adaptive FIR filtering. The coefficients of the FIR filter are updated using the LMS algorithm, which automatically isolates the outlier image regions by decreasing the corresponding coefficients. The adaptation criterion of the LMS estimator is the error between the median of the samples from the LR images and the output of the FIR filter. Through simulated experiments on synthetic images and on real camera images, we show that the proposed technique performs well in the presence of motion outliers. This relatively simple and fast mechanism enables to add robustness in practical implementations of image super-resolution, while still being effective against Gaussian noise in the image formation model. Copyright © 2006 Mejdi Trimeche et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Nowadays, digital cameras are being integrated into more versatile and portable computing platforms such as cameraphones or PDA’s. Often, the intrinsic image quality is limited due to packaging and pricing constraints. On the other hand, the computational and memory resources on mobile devices are increasing all the time. It is already possible to consider the implementation of sophisticated and computationally intensive image processing algorithms. Super-resolution (SR) [1–3] is considered to be one of the most promising techniques that can help overcome the limitations due to optics and sensor resolution. The technique consists in combining a set of low-resolution (LR) images portraying slightly different views of the same scene in order to reconstruct a high-resolution (HR) image of that scene. The idea is to increase the information content in the final image by exploiting the additional spatio-temporal information that is available in each of the LR images. In practice, the quality of the super-resolved images depends heavily on the accuracy of the motion estimation; in fact, subpixel precision in the motion field is needed to achi
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