Feature-Based Affine Motion Estimation for Superresolution of a Region of Interest

Feature-Based Affine Motion Estimation for Superresolution of a Region of Interest

Sung Hyun Kim, Rae-Hong Park, Seungjoon Yang, Hwa-Young Kim
ISBN13: 9781466687899|ISBN10: 1466687894|EISBN13: 9781466687905
DOI: 10.4018/978-1-4666-8789-9.ch031
Cite Chapter Cite Chapter

MLA

Kim, Sung Hyun, et al. "Feature-Based Affine Motion Estimation for Superresolution of a Region of Interest." Human-Computer Interaction: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 682-701. https://doi.org/10.4018/978-1-4666-8789-9.ch031

APA

Kim, S. H., Park, R., Yang, S., & Kim, H. (2016). Feature-Based Affine Motion Estimation for Superresolution of a Region of Interest. In I. Management Association (Ed.), Human-Computer Interaction: Concepts, Methodologies, Tools, and Applications (pp. 682-701). IGI Global. https://doi.org/10.4018/978-1-4666-8789-9.ch031

Chicago

Kim, Sung Hyun, et al. "Feature-Based Affine Motion Estimation for Superresolution of a Region of Interest." In Human-Computer Interaction: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 682-701. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8789-9.ch031

Export Reference

Mendeley
Favorite

Abstract

This chapter presents an interpolation method of low-computation for a Region Of Interest (ROI) using multiple low-resolution images of the same scene. Interpolation methods using multiple images require the accurate motion information between the reference image of interpolation and the other images. Sometimes complex local motions applied to the entire images are estimated incorrectly, yielding seriously degraded interpolation results. The authors apply the proposed Superresolution (SR) method, which employs a simple global motion model, only to the ROI that contains important information of the scene. The ROIs extracted from multiple images are assumed to have simple global motions. At first, using a mean absolute difference measure, they extract the regions from the multiple images that are similar to the selected ROI in the reference image of interpolation and use feature points to estimate the affine motion parameters. The authors apply the Projection Onto Convex Sets (POCS)-based method to the ROI using the estimated motion, simplify the iterative computation of the whole system, and use an edge-preserving smoothing filter to reduce the distortion caused by additive noise. In experiments, they acquire test image sets with a hand-held digital camera and use a Gaussian noise model. Experimental results show that the feature-based Motion Estimation (ME) is accurate and reducing the computational load of the ME step is efficient in terms of the computational complexity. It is also shown that the SR results using the proposed method are remarkable even when input images contain complex motions and a large amount of noise. The proposed POCS-based SR algorithm can be applied to digital cameras, portable camcorders, and so on.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.