Proceedings Article10.1109/ICIG.2004.8
A MAP algorithm to super-resolution image reconstruction
Huanfeng Shen,Pingxiang Li,Liangpei Zhang,Yindi Zhao +3 more
- 18 Dec 2004
- pp 544-547
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TL;DR: A new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low-resolution images that have been sub-sampled and displaced by different amounts of sub-pixel shifts, based on the MAP framework.
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Abstract: Super-resolution image reconstruction has been one of the most active research areas in recent years. In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low-resolution images that have been sub-sampled and displaced by different amounts of sub-pixel shifts. The algorithm is based on the MAP framework, solving the optimization by proposed iteration steps. At each iteration step, the regularization parameter is updated using the partially reconstructed image solved at the last step. The proposed algorithm is tested on synthetic images, and the reconstructed images are evaluated by the PSNR method. The results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.
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Citations
Super-Resolution of Remotely Sensed Images With Variable-Pixel Linear Reconstruction
M.T. Merino,Jorge Núñez +1 more
TL;DR: It is shown that the Super-Resolution Variable-Pixel Linear Reconstruction algorithm can make significant spatial resolution improvements to satellite images of the Earth's surface allowing recognition of objects with size approaching the limiting spatial resolution of the lower resolution images.
An Adaptive Subpixel Mapping Method Based on MAP Model and Class Determination Strategy for Hyperspectral Remote Sensing Imagery
TL;DR: An adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery to improve the accuracy and regularize the ill-posed subpixels mapping problem.
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POCS Based Super-Resolution Image Reconstruction Using an Adaptive Regularization Parameter
TL;DR: An adaptive regularization approach based on the fact that the regularization parameter should be a linear function of noise variance is proposed and the obtained results demonstrate the superiority of the approach compared with existing methods.
Novel Super Resolution Restoration of Remote Sensing Images Based on Compressive Sensing and Example Patches-Aided Dictionary Learning
Shuyuan Yang,Fenghua Sun,Min Wang,Zhizhou Liu,Licheng Jiao +4 more
- 20 Jan 2011
TL;DR: In this paper, a machine learning and compressive sensing (CS) based super-resolution (SR) algorithm for the restoration of remote sensing images is proposed, which relies on the idea that high-resolution image patches can be correctly recovered from the downsampled low- resolution (LR) image patches under two mild conditions, i.e., the sparsity of image patches, and the incoherence between the sensing and projection matrix.
41
A modular neural network for super-resolution of human faces
TL;DR: The original and versatile architecture of a modular neural network and its application to super-resolution is presented and it is shown that the network performs global-scale reconstruction of human faces from very low resolution input images.
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