Journals / Eskişehir Osmangazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2010 / Cilt: 23 - Sayı: 2

Uyarlanabilir aykırı değer ayıklama yöntemiyle kenar korumalı yüksek çözünürlük

Edge-preserving super-resolution using an adaptive outlier rejection method

Pages
35–51
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The method is shown to reduce over-blurring caused by the regularization that is common in iterative super-resolution restoration algorithms.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Registration errors are well-known problems in super-resolution restoration applications.
📊
Key Findings & Data:Local outliers are caused by the registration errors and objects in motion.
💡
Conclusion & Impact:The method is shown to reduce over-blurring caused by the regularization that is common in iterative super-resolution restoration algorithms.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Registration errors are well-known problems in super-resolution restoration applications. Local outliers are caused by the registration errors and objects in motion. Instead of blind rejection of local outliers, we favor for the detected edges. For that, pre-estimated high-resolution image is searched for some specified edge and corner patterns. Outlier rejection is performed based on the pattern found. The method is shown to reduce over-blurring caused by the regularization that is common in iterative super-resolution restoration algorithms.

Özet

İmge çakıştırma hataları yüksek çözünürlük uygulamalarında önemli bir problem oluşturmaktadır. Yerel aykırı değerler sahne İçindeki hareketli nesneler ve çakıştırma hatalarından kaynaklanmaktadır. Bu çalışmada, aykırı değerler bir dayanak olmadan atılmak yerine, resimler içinde belirlenen kenarlar ve köşeler gözönüne alınarak ve bulunan yerel yapıya göre işlem dışında tutulmaktadır. Önerilen yöntemin yinelemeli yüksek çözünürlük algorimalarındct sıklıkla kullanılan düzenleyicilerden doğan aşırı bulanmayı azalttığı deneysel olarak gösterilmiştir.

🔖

Cite This Article

SEKE, E., ÖZKAN, K. (2022). Uyarlanabilir aykırı değer ayıklama yöntemiyle kenar korumalı yüksek çözünürlük. Eskişehir Osmangazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 23 - Sayı: 2, 35–51. https://ajindex.com/articles/505789

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. B. C. Tom and A. K. Katsaggelos, "Reconstruction of a High-Resolution Image by Simultaneous Registration, Restoration, and Interpolation of Low-Resolution Images,1' Proc. 1995 IEEE International Conf. on Image Processing, pp. H-539-542, Oct. 1995, Washington, DC
  2. H.S. Hou, H.C. Andrews, "Cubic splines for image interpolation and digital filtering," IEEE Transactions on Acoustics, Speech, Signal Processing ÁSSP-26, Vol.6, pp. 508-517, 1978
  3. R. Y. Tsai and T. S. Huang. "Multiframe image restoration and registration," In R. Y. Tsai and T. S. Huang, editors, Advances in Computer Vision and Image Processing, Vol.1, pp. 317-339. JAI Press Inc., 1984
  4. M. Irani and S. Peleg, "Improving Resolution by Image Registration," Computer Vision, Graphics and Image Processing, vol.53, pp. 231-239, May 1991.- [5] IC.P. Hong, J.K. Paik, H. Ju Kim, C. Ho Lee, "An edge-preserving image interpolation system for a digital camcorder", IEEE Transactions on Consumer Electronics, Vol.42, No.3, 1996
  5. Battiato S., Gallo G., Stanco F., "A Locally-Adaptive Zooming Algorithm for Digital Images", Elsevier Image Vision and Computing Journal, Vol.20, No. 11, pp.805-812, 2002
  6. C. Bauman, K. Sauer, "A Generalized Gaussian Image Model for Edge-Preserving MAP Estimation", IEEE Transactions on Image Processing, Vol.2, No.3, pp.296-310, July 1993
  7. S. Tebaul, L. Blanc-Féraud, G. Aubert, M. Barlaud, "Variational approach for edge-preserving regularization using coupled PDE's", IEEE Transactions on Image Processing, Vol.7, No.3, pp.387-397, 1998
  8. M. Beige, M.E. Kilmer. E.L. Miller, "Wavelet domain image restoration with adaptive edge-preserving regularization", IEEE Transactions on Image Processing, Vol.9, No.4, pp.597-608, 2000
  9. M.K. Ng, N.K. Boze, "Mathematical Analysis of Super-Resolution Methodology", IEEE Signal Processing Magazine, Vol.20, No.3, pp. 62-74, 2003
  10. R. Pan, S.J. Reeves, "Efficient huber-markov edge-preserving image restoration", IEEE Transactions on Image Processing, Vol.5, No.2, pp.3728-3735, 2006
  11. R. K. Ward, "Restorations of Differently Blurred Versions of an Image with Measurement Errors in the PSF's", IEEE Transactions on Image Processing, Vol.2, No.3, pp.369-381, 1993
  12. M. Elad and A. Feuer, "Restoration of a Single Superresolution Image from Several Blurred, Noisy and Undersampled Measured Images", IEEE Transactions on Image Processing, Vol.6, No.12, pp.1646-1658,1997
  13. M. K. Ozkan, A. M. Tekalp and M. I. Sezan, "POCS Ba