Texture and edge preserving multiframe super-resolution

Date

2014-09-01

Author

Turgay, Emre
Akar, Gözde

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Super-resolution (SR) image reconstruction refers to methods where a higher resolution image is reconstructed using a set of overlapping aliased low-resolution observations of the same scene. Although edge preservation has been a widely explored topic in SR literature, texture-specific regularisation has recently gained interest. In this study, texture-specific regularisation is handled as a post-processing step. A two stage method is proposed, comprising multiple SR reconstructions with different regularisation parameters followed by a restoration step for preserving edges and textures. In the first stage, two maximum-aposteriori estimators with two different amounts of regularisation are employed. In the second stage, pixel-to-pixel difference between these two estimates is post-processed to restore edges and textures. Frequency selective characteristics of discrete cosine transform and Gabor filters are utilised in the post-processing step. Experiments on synthetically generated images and real experiments demonstrate that the proposed methods give better results compared with the state-of-the-art SR methods especially on textures and edges.

Subject Keywords

Signal Processing, Electrical and Electronic Engineering, Software, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/34722

Journal

IET IMAGE PROCESSING

DOI

https://doi.org/10.1049/iet-ipr.2013.0342

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

E. Turgay and G. Akar, “Texture and edge preserving multiframe super-resolution,” IET IMAGE PROCESSING, pp. 499–508, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34722.