Authors:
Yoshiki Kakamu
;
Takahiro Maruyama
and
Kazuhiro Hotta
Affiliation:
Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan
Keyword(s):
Super-Resolution, Carbon Nanotube, Shift, SwinIR, Re-Learning.
Abstract:
In this study, we perform super-resolution of carbon nanotube images using Deep Learning. In order to achieve super-resolution with higher accuracy than conventional SwinIR, we introduce an encoder-decoder structure to input an image of larger size and a Shift mechanism for local feature extraction. In addition, we propose super-resolution method by re-training to perform super-resolution with high accuracy even with a small number of images. Experiments were conducted on DIV2K, General100, Set5, and carbon nanotube image dataset for evaluation. Experimental results confirmed that the proposed method provides higher accuracy than the conventional SwinIR, and showed that the proposed method can super-resolve carbon nanotube images. The main contribution is the proposal of a network model with better performance for super-resolution of carbon nanotube images even if there is no crisp supervised images. The proposed method is suitable for such images. Effectiveness of our method was dem
onstrated by experimental results on a general super-resolution dataset and a carbon nanotube image dataset.
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