Learning stacking regressors for single image super-resolution-Reference-Cited by-同舟云学术

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Learning stacking regressors for single image super-resolution

Published:2020-07-18 Issue:12 Volume:50 Page:4325-4341
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Zhang Kaibing,Luo Shuang,Li Minqi,Jing Junfeng,Lu Jian,Xiong Zenggang

Funder

National Natural Science Foundation of China

Key Project of the Natural Science Foundation of Shaanxi Province

Doctoral Startup Foundation of Xi’an Polytechnic University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-020-01787-0.pdf

Reference49 articles.

1. Huang T S, Tsai RY (1984) Multiframe image restoration and registration. Adv Comput Visual Image Process 1(2):317–339

2. Hou H, Andrews H (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans Acoustics, Speech Signal Process 26(6):508–517

3. Zhou D, Shen X, Dong W (2012) Image zooming using directional cubic convolution interpolation. IET Image Process 6(6):627–634

4. Liu Y, Zhang Y, Guo Q, Zhang C (2014) Image interpolation based on weighted and blended rational function. In: Asian conference on computer vision. Springer, Cham, pp 78–88

5. Zhang Y, Fan Q, Bao F, Liu Y, Zhang C (2018) Single-image super-resolution based on rational fractal interpolation. IEEE Trans Image Process 27(8):3782–3797

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