Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Enhancement method with naturalness preservation and artifact suppression based on an improved Retinex variational model for color retinal images

Not Accessible

Your library or personal account may give you access

Abstract

Retinal images are widely used for the diagnosis of various diseases. However, low-quality retinal images with uneven illumination, low contrast, or blurring may seriously interfere with diagnosis by ophthalmologists. This study proposes an enhancement method for low-quality retinal color images. In this paper, an improved variational Retinex model for color retinal images is first proposed and applied to each channel of the RGB color space to obtain the illuminance and reflectance layers. Subsequently, the Naka–Rushton equation is introduced to correct the illumination layer, and an enhancement operator is constructed to improve the clarity of the reflectance layer. Finally, the corrected illuminance and enhanced reflectance are recombined. Contrast-limited adaptive histogram equalization is introduced to further improve the clarity and contrast. To demonstrate the effectiveness of the proposed method, this method is tested on 527 images from four publicly available datasets and 40 local clinical images from Tianjin Eye Hospital (China). Experimental results show that the proposed method outperforms the other four enhancement methods and has obvious advantages in naturalness preservation and artifact suppression.

© 2022 Optica Publishing Group

Full Article  |  PDF Article
More Like This
Joint Retinex-based variational model and CLAHE-in-CIELUV for enhancement of low-quality color retinal images

Zongheng Huang, Chen Tang, Min Xu, and Zhenkun Lei
Appl. Opt. 59(28) 8628-8637 (2020)

CODEN: combined optimization-based decomposition and learning-based enhancement network for Retinex-based brightness and contrast enhancement

Sangjae Ahn, Joongchol Shin, Heunseung Lim, Jaehee Lee, and Joonki Paik
Opt. Express 30(13) 23608-23621 (2022)

Data availability

Data underlying the results presented in this paper are available in Refs. [5053].

50. J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. VanGinneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23, 501–509 (2004). [CrossRef]  

53. T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, and J. Pietil, “DIARETDB1 diabetic retinopathy database and evaluation protocol,” in Proceedings of the British Machine Vision Conference (2007).

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

Figures (8)

You do not have subscription access to this journal. Figure files 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

Tables (3)

You do not have subscription access to this journal. Article tables 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

Equations (16)

You do not have subscription access to this journal. Equations 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, including rights for text and data mining and training of artificial technologies or similar technologies.