An iterative super-resolution reconstruction of image sequences using affine block-based registration

Authors:
V. Patanavijit;S. Jitapunkul
Affiliations:
Assumption University, Bangkok, Thailand;Chulalongkorn University, Bangkok, Thailand
Venue:
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Year:
2006

Citing 8
Cited 1

Super-Resolution Reconstruction of Image Sequences

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of high-resolution frames from video sequences

IEEE Transactions on Image Processing
Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images

IEEE Transactions on Image Processing
A computationally efficient superresolution image reconstruction algorithm

IEEE Transactions on Image Processing
A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur

IEEE Transactions on Image Processing
Fast and robust multiframe super resolution

IEEE Transactions on Image Processing
Regularized constrained total least squares image restoration

IEEE Transactions on Image Processing

Super-resolution image reconstruction techniques: Trade-offs between the data-fidelity and regularization terms

Information Fusion

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Abstract

Super-resolution reconstruction produces one or a set of high-resolution (HR) images from a sequence of low-resolution (LR) images. Due to translational registration, super-resolution reconstruction can apply only on the sequences that have simple translation motion. This paper proposed a novel super-resolution reconstruction that that can apply on real sequences or complex motion sequences. The proposed super-resolution reconstruction uses a high accuracy registration algorithm, the fast affine block-based registration [16], in the maximum likelihood framework. Moreover, the regularization is used to compensate the missing measurement information. The experimental results show that the proposed reconstruction can apply on real sequence such as Foreman and Suzie.