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Single Image Super-Resolution using Residual Image based on CNN with a Deconvolutional Layer디컨볼루셔널 레이어를 포함하는 CNN 기반 레지듀얼 이미지 이용 단일 영상 초해상도 복원
- Other Titles
- 디컨볼루셔널 레이어를 포함하는 CNN 기반 레지듀얼 이미지 이용 단일 영상 초해상도 복원
- Authors
- Kang Ho Shin; Woo Jin Jeong; Young Shik Moon
- Issue Date
- Jun-2016
- Publisher
- 대한전자공학회
- Citation
- 2016년 대한전자공학회 하계학술대회 논문집, pp 978 - 981
- Pages
- 4
- Indexed
- OTHER
- Journal Title
- 2016년 대한전자공학회 하계학술대회 논문집
- Start Page
- 978
- End Page
- 981
- Abstract
- This paper proposes a method for recovering the intrinsic details of an image that cannot be reconstructed by interpolation, named as residual images, through a convolutional neural network with a deconvolutional layer. The predicted residual image is added to an interpolated LR image to reconstruct the lost details. In both the qualitative and quantitative comparison to SRCNN, the proposed framework performed in a better manner. The proposed framework did not produce the false edges seen in the results of SRCNN. Furthermore, the proposed method resulted 0.18 dB higher PSNR in average, compared to SRCNN.
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