Results 221 to 230 of about 30,721 (259)

Super resolution based on simultaneous registration and reconstruction

2008 10th International Conference on Control, Automation, Robotics and Vision, 2008
The progress of super-resolution usually involves computing the parameters (e.g. the registration and blur parameters) of a generating imaging model, and obtaining an estimated SR image by minimizing a cost function. In this paper, a high-resolution image, the registration parameters and PSF width will be optimized simultaneously using scaled conjugate
Zhi Yuan, Peimin Yan, Sheng Li, Juxia Shen, Yuhui Huang   +4 more
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Super resolution image reconstruction and imaging device

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016
Super resolution (SR) studies started in the 1990s and many papers were issued in the 2000s. Super resolution image reconstruction (SRR) is one of the most common SR methods. SRR reconstructs a high-resolution image (HRI) using multiple low-resolution images (LRIs).
Chinatsu Mori, Kenkichi Tanioka, Seiichi Gohshi   +2 more
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Regularization for Super-Resolution Image Reconstruction

2006
Super-resolution image reconstruction estimates a high-resolution image from a sequence of low-resolution, aliased images. The estimation is an inverse problem and is known to be ill-conditioned, in the sense that small errors in the observed images can cause large changes in the reconstruction.
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A GPU-based implementation on super-resolution reconstruction

2012 19th IEEE International Conference on Image Processing, 2012
Super-resolution reconstruction (SRR) proposes a fusion of several low-quality images into one higher quality result with better optical resolution. However, due to the vast amount of calculation of the SRR algorithm, its implementation is too slow. In this paper, we present a GPU-based parallel implementation on SRR algorithm.
Kai Wang, Lifu Wang, Jian Lu, Yi Sun 0009, Shuping Zhao   +4 more
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An Overview of Image Super-Resolution Reconstruction Algorithm

2018 11th International Symposium on Computational Intelligence and Design (ISCID), 2018
Super-resolution image reconstruction refers to the reconstruction of a clear (or multiple) high-resolution image from a low-resolution degraded image (or image sequence) of the same scene. This technology has become a research hotspot in the field of image processing. However, it is difficult to break through the traditional method.
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Super-resolution reconstruction of continuous image sequences

Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 2003
Super-resolution reconstruction algorithms perform a fusion of several low quality images of the same scene into a single improved quality image. As opposed to this STATIC recovery problem, in this paper we define a DYNAMIC super-resolution task: the restoration of a blurred, decimated, and noisy image sequence.
Michael Elad, Arie Feuer
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Super-resolution reconstruction in MRI

2012
Improving the resolution in magnetic resonance imaging (MRI) is always done at the expense of either the signal-to-noise ratio (SNR) or the acquisition time. This study investigates whether so-called super-resolution reconstruction (SRR) is an advantageous alternative to direct high-resolution (HR) acquisition in terms of the SNR and acquisition time ...
Plenge, Esben, Poot, Dirk H.J., Bernsen, Monique, Kotek, Gyula, Houston, Gavin, Wielopolski, Piotr, Van Der Weerd, Louise, Niessen, Wiro J., Meijering, Erik   +8 more
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Reconstruction and super-resolution of dilute aperture imagery

Proceedings of 3rd IEEE International Conference on Image Processing, 2002
Improving the resolving power of diffraction-limited imaging devices has most often been accomplished with costly increases in the size of the imaging aperture. To minimize cost and construction difficulty yet still improve image resolution we use a dilute aperture imaging system followed by post-processing with the Poisson maximum a-posteriori (PMAP ...
Casey Miller, Bobby R. Hunt, R. L. Kendrick, A. L. Duncan   +3 more
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HOS-Based Image Super-Resolution Reconstruction

2007
In this paper a novel high-order statistics (HOS) based regularized algorithm for image super-resolution reconstruction is proposed. In this method, the image is divided into various regions according to the local forth order statistics. The segmentation label is then used to determine the weighted operator of the regularization term.
Jianping Qiao, Ju Liu
openaire   +1 more source

Context based super resolution image reconstruction

2009 International Workshop on Local and Non-Local Approximation in Image Processing, 2009
In this paper a context based super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator identifies local gradients and textures for selecting the optimal SR method for the region of interest. Texture segmentation and gradient map estimation are done prior to the reconstruction stage. Gradient
Turgay, Emre, Akar, Gözde
openaire   +2 more sources