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DCID: A divide and conquer approach to solving the trade-off problem between artifacts caused by enhancement procedure in image downscaling

Authors
Kang, Eun SuChae, Yeon JeongPark, Jae HyeonCho, Sung In
Issue Date
Aug-2024
Publisher
Elsevier BV
Keywords
Artifact-least; Deep neural network; Image downscaling
Citation
Signal Processing: Image Communication, v.126, pp 1 - 11
Pages
11
Indexed
SCIE
SCOPUS
Journal Title
Signal Processing: Image Communication
Volume
126
Start Page
1
End Page
11
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/22020
DOI
10.1016/j.image.2024.117133
ISSN
0923-5965
1879-2677
Abstract
Conventional research on image downscaling is conducted to improve the visual quality of the resultant downscaled image. However, there is an intractable problem, a trade-off relationship between artifacts such as aliasing and ringing, caused by enhancement procedure in image downscaling. To solve this problem, we propose a novel method that applies a divide-and-conquer approach for image downscaling (DCID). Specifically, the proposed DCID includes Weight-Net for dividing regions into enhancement first and artifact-least first regions and two generators that are optimized for divided regions to conquer the trade-off problem in the image downscaling task. The proposed method can generate a downscaled image without creating artifacts while preserving the perceptual quality of the input image. In objective and subjective evaluations, our experimental results show that the quality of the downscaled images generated by the proposed DCID is significantly better than benchmark methods. © 2024 Elsevier B.V.
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