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Texture and edge preserving multiframe super-resolution
Date
2014-09-01
Author
Turgay, Emre
Akar, Gözde
Metadata
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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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BibTeX
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.