Dergiler / Balkan Journal of Electrical and Computer Engineering / 2020 / Cilt: 8 - Sayı: 3

Single-Image Super-Resolution Analysis in DCT Spectral Domain

Sayfa
209–217
Yayın Tarihi
2022-10-11
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

We evaluate our approach in detail through quantitative and qualitative results.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Advances in deep learning techniques have lead to drastic changes in contemporary methods used for a di-verse number of computer vision problems.
📊
Temel Bulgular & Veri:Single-image super-resolution is one of these problems that has been significantly and positively influenced by these trends.
💡
Sonuç & Katkı:We evaluate our approach in detail through quantitative and qualitative results.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Advances in deep learning techniques have lead to drastic changes in contemporary methods used for a di-verse number of computer vision problems. Single-image super-resolution is one of these problems that has been significantly and positively influenced by these trends. The mainstream state-of-the-art methods for super-resolution learn a non-linear mapping from low-resolution images to high-resolution images in the spatial domain, parameterized through convolution and transposed-convolution layers. In this paper, we explore the use of spectral representations for deep learning based super-resolution. More specifically, we propose an approach that operates in the space of discrete cosine transform based spectral representations. Additionally, to reduce the artifacts resulting from spectral processing, we propose to use a noise reduction network as a post-processing step. Notably, our approach allows using a universal super-resolution model for a range of scaling factors. We evaluate our approach in detail through quantitative and qualitative results.

🔖

Bu Makaleye Atıf Yap

AYDIN, O., CINBIS, R. G. (2022). Single-Image Super-Resolution Analysis in DCT Spectral Domain. Balkan Journal of Electrical and Computer Engineering, Cilt: 8 - Sayı: 3, 209–217. https://ajindex.com/articles/460513

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  1. R. Timofte, V. De Smet, and L. Van Gool, “A+: Adjusted anchored neighborhood regression for fast super-resolution,” in Asian Conference on Computer Vision. Springer, 2014, pp. 111–126
  2. J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE International Conference on Image Processing, vol. 19, no. 11, pp. 2861–2873, 2010
  3. S. Schulter, C. Leistner, and H. Bischof, “Fast and accurate image up-scaling with super-resolution forests,” in IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3791–3799
  4. C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 295–307, 2016
  5. J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” in IEEE Conference on Com-puter Vision and Pattern Recognition, 2016, pp. 1646–1654
  6. W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Deep laplacian pyramid networks for fast and accurate super-resolution,” arXiv preprint arXiv:1704.03915, 2017
  7. S. Anwar, S. Khan, and N. Barnes, “A deep journey into super-resolution: A survey,” arXiv preprint arXiv:1904.07523, 2019
  8. O. Rippel, J. Snoek, and R. P. Adams, “Spectral representations for convolutional neural networks,” in Advances in Neural Information Processing Systems, 2015, pp. 2449–2457
  9. Y. Wang, C. Xu, S. You, D. Tao, and C. Xu, “Cnnpack: Packing convolutional neural networks in the frequency domain,” in Advances in Neural Information Processing Systems, 2016, pp. 253–261
  10. N. Kumar, R. Verma, and A. Sethi, “Convolutional neural networks for wavelet domain super resolution,” Pattern Recognition Letters, vol. 90, pp. 65–71, 2017
  11. J. Li, S. You, and A. Robles-Kelly, “A frequency domain neural network for fast image super-resolution,” in International Joint Conference on Neural Networks. IEEE, 2018, pp. 1–8
  12. S. Xue, W. Qiu, F. Liu, and X. Jin, “Faster image super-resolution by improved frequency-domain neural networks,” Signal, Image and Video Processing, pp. 1–9, 2019
  13. C. Dong, C. C. Loy, and X. Tang, “Accelerating the super-resolution convolutional neural network,” in European Conference on Computer Vision. Springer, 2016, pp. 391–407
  14. C. Ledig, L. Theis, F. Huszar,´ J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al., “Photo-realistic single image super-r