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32‐2: Improvement in Directional Cubic Convolution Image Interpolation
Author(s) -
Liu Xiaolei,
Sun Jiankang,
Yan Guixin,
Lu Yaoyu,
Xue Yachong,
Li Gang,
Chen Lili,
Zhang Hao
Publication year - 2020
Publication title -
sid symposium digest of technical papers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.351
H-Index - 44
eISSN - 2168-0159
pISSN - 0097-966X
DOI - 10.1002/sdtp.13903
Subject(s) - bicubic interpolation , image scaling , stairstep interpolation , interpolation (computer graphics) , convolution (computer science) , distortion (music) , artificial intelligence , computer vision , nearest neighbor interpolation , computer science , enhanced data rates for gsm evolution , bilinear interpolation , algorithm , demosaicing , mathematics , trilinear interpolation , image (mathematics) , multivariate interpolation , image processing , color image , artificial neural network , amplifier , computer network , bandwidth (computing)
Compared with traditional methods, edge‐based interpolation algorithm can generate high‐resolution images with rare edge artifacts. However complex textured regions produce morphological distortion at the same time, resulting in a HD image looks unnatural. In this paper, we analyze the disadvantages of the best performing edge‐based interpolation (DCCI) algorithm and propose a novel adaptive edge‐based interpolation algorithm, which use both 2‐D texture entropy and directional gradient to eliminate the distortion. It turns out that this method can effectively improve the image quality.

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