Image Super Resolution Using Fractal Coding and Residual Network
Joint Authors
Li, Jinjiang
Hua, Zhen
Zhang, Haicheng
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-28
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Fractal coding techniques are an effective tool for describing image textures.
Considering the shortcomings of the existing image super-resolution (SR) method, the large-scale factor reconstruction performance is poor and the texture details are incomplete.
In this paper, we propose an SR method based on error compensation and fractal coding.
First, quadtree coding is performed on the image, and the similarity between the range block and the domain block is established to determine the fractal code.
Then, through this similarity relationship, the attractor is reconstructed by super-resolution fractal decoding to obtain an interpolated image.
Finally, the fractal error of the fractal code is estimated by the depth residual network, and the estimated version of the error image is added as an error compensation term to the interpolation image to obtain the final reconstructed image.
The network structure is jointly trained by a deep network and a shallow network.
Residual learning is introduced to greatly improve the convergence speed and reconstruction accuracy of the network.
Experiments with other state-of-the-art methods on the benchmark datasets Set5, Set14, B100, and Urban100 show that our algorithm achieves competitive performance quantitatively and qualitatively, with subtle edges and vivid textures.
Large-scale factor images can also be reconstructed better.
American Psychological Association (APA)
Hua, Zhen& Zhang, Haicheng& Li, Jinjiang. 2019. Image Super Resolution Using Fractal Coding and Residual Network. Complexity،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1133246
Modern Language Association (MLA)
Hua, Zhen…[et al.]. Image Super Resolution Using Fractal Coding and Residual Network. Complexity No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1133246
American Medical Association (AMA)
Hua, Zhen& Zhang, Haicheng& Li, Jinjiang. Image Super Resolution Using Fractal Coding and Residual Network. Complexity. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1133246
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133246