Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 2,
  • Issue 9,
  • pp. 512-515
  • (2004)

Super-resolution image restoration algorithm based on orthogonal discrete wavelet transform

Not Accessible

Your library or personal account may give you access

Abstract

By using orthogonal discrete wavelet transform (ODWT) and generalized cross validation (GCV), and combining with Luck-Richardson algorithm based on Poisson-Markov model (MPML), several new super-resolution image restoration algorithms are proposed. According to simulation experiments for practical images, all the proposed algorithms could retain image details better than MPML, and be more suitable to low signal-to-noise ratio (SNR) images. The single operation wavelet MPML (SW-MPML) algorithm and MPML algorithm based on single operation wavelet transform (MPML-SW) avoid the iterative operation of self-adaptive parameter in MPML particularly, and improve operating speed and precision. They are instantaneous to super-resolution image restoration process and have extensive application foreground.

© 2005 Chinese Optics Letters

PDF Article
More Like This
Confocal pore size measurement based on super-resolution image restoration

Dali Liu, Yun Wang, Lirong Qiu, Xinyue Mao, and Weiqian Zhao
Appl. Opt. 53(25) 5694-5700 (2014)

Improving spatial resolution of confocal Raman microscopy by super-resolution image restoration

Han Cui, Weiqian Zhao, Yun Wang, Ying Fan, Lirong Qiu, and Ke Zhu
Opt. Express 24(10) 10767-10776 (2016)

Dual tree complex wavelet transform based denoising of optical microscopy images

Ufuk Bal
Biomed. Opt. Express 3(12) 3231-3239 (2012)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved