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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.4
- 수록면
- 482 - 489 (8page)
- DOI
- 10.9717/kmms.2024.27.4.482
이용수
초록· 키워드
오류제보하기
Recently, with the development of artificial neural networks, deep convolutional neural network (CNN) has been successfully introduced in the single image super-resolution (SISR) field. Among several CNN-based image restoration methods, area-specific convolutional neural network (ASCNN) is proposed based on the observation that low-frequency pixel areas have relatively less influence on image restoration quality than high-frequency pixel areas. After ASCNN analyzes the spatial frequencies of an image, it applies a relatively simpler convolution operation to the low-frequency region than to the high-frequency region. However, this method cannot perform optimized operations on each pixel. In this paper, multi-path method is proposed which further subdivides the image area into more than two areas. By performing operations only as needed according to the frequency level of each pixel, FLOPs (floating-point operations) can be efficiently reduced even with a slight decrease in performance. According to experimental results, instead of a decrease of approximately 0.43% in PSNR (peak-topeak signal ratio), FLOPs is reduced by up to 42%.
목차
ABSTRACT
1. 서론
2. Area-Specific Convolutional Neural Networks
3. 제안한 방법
4. 실험 결과 및 고찰
5. 결론
REFERENCE
참고문헌 (14)
참고문헌 신청
D. Lee, H. Lee, K. Lee, and H. Lee, “Fast Very Deep Convolutional Neural Network with Deconvolution for Super-Resolution,” Journal of Korea Multimedia Society, Vol. 20, No. 11, pp. 1750-1758, 2017.
Y. Seo and S. Gang, “Latest Trends of Deep Learning-Based Super-Resolution Technology,” Broadcasting and Media, Vol. 25, No. 2, pp. 7-16, 2020.
S. Fadnavis, “Image Interpolation Techniques in Digital Image Processing: An Overview,” International Journal of Engineering Research and Applications, Vol. 4, No. 10, pp. 70-73, 2014.
R. Timofte, V. Smet, and L. Gool, “Anchored Neighborhood Regression for Fast Example-Based Super-Resolution,” Proceedings of the IEEE International Conference on Computer Vision, pp. 1920-1927, 2014.
R. Timofte, V. Smet, and L. Gool, “A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution,” Computer Vision-ACCV 2014: 12th Asian Conference on Computer Vision, pp. 111-126, 2015.
Y. Seo and S. Gang, “Latest Trends of Deep Learning-Based Super-Resolution Technology,” Broadcasting and Media, Vol. 25, No. 2, pp. 7-16, 2020.
S. Fadnavis, “Image Interpolation Techniques in Digital Image Processing: An Overview,” International Journal of Engineering Research and Applications, Vol. 4, No. 10, pp. 70-73, 2014.
R. Timofte, V. Smet, and L. Gool, “Anchored Neighborhood Regression for Fast Example-Based Super-Resolution,” Proceedings of the IEEE International Conference on Computer Vision, pp. 1920-1927, 2014.
R. Timofte, V. Smet, and L. Gool, “A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution,” Computer Vision-ACCV 2014: 12th Asian Conference on Computer Vision, pp. 111-126, 2015.
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