Use of diffusing filters for artificially reducing visual acuity when testing equipment and procedures

  • Sven P. HeinrichEmail author
  • Isabell Strübin
Technical Note



When evaluating ophthalmological devices and procedures, for instance those for visual electrophysiology, it is often desirable to perform tests with reduced acuity. Doing this with individuals with actual visual impairments has a number of disadvantages, such as considerable recruitment efforts, especially when a specific acuity range is targeted, and little control about the actual perceptual characteristics of the impairment, which are normally not fully known. Lenses with positive diopters or blurring filters that are placed in front of the eyes of visually normal observers promise a simple solution to the problem. However, defocus results in considerable spurious resolution, and previous studies suggest that the frequently used Bangerter occluders are not optimal for the purpose. The present study therefore reviews a number of other options and tests a selection of filters with respect to their effect on acuity and contrast sensitivity with the aim of identifying filters that primarily degrade acuity while mostly sparing contrast sensitivity.


First, we screened several filters for potential usefulness. The Freiburg Acuity and Contrast Test was then used to measure visual acuity and contrast sensitivity with a subset of three filters (Luminit LSD 0.5° and 1°, and LEE 420) and, for comparison, with a Bangerter occluder with a nominal acuity grade of 0.1. A qualitative comparison of the filters’ effect on the checkerboard-reversal VEP was also performed.


With both Luminit filters, variability in acuity across participants was relatively small, and at least with the 0.5° version, contrast sensitivity was relativity little affected. The LEE filter and the Bangerter occluder resulted in more variability and, compared to the effect on acuity, a relatively strong reduction in contrast sensitivity. Comparing the Luminit 0.5° and 1° filters, the reduction of acuity was not proportional to physical stimulus degradation. The effect on VEP responses was consistent with the psychophysical data.


The Luminit filters, which have a Gaussian light diffusion profile, appear to be a good choice for artificial reduction of acuity.


Visual acuity Contrast sensitivity Occlusion Blur Observer method Artificial degradation 



We thank Verena Gauggel for their competent assistance with data acquisition, Patrick Weisert for making the holders that allowed for inserting the filters into regular trial frames (Fig. 2), and Gottfried Martin for support with the micrographs of the filters (Fig. 1). We are also grateful to the volunteers who participated in the study. Optrovision, Munich, Germany, a distributor of Luminit diffusers, provided free-of-charge samples.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee (Ethik-Kommission der Albert-Ludwigs-Universität Freiburg, application number 622/14) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Data availability

Data are available on request from the authors.


  1. 1.
    Towle VL, Harter MR (1977) Objective determination of human visual acuity: pattern evoked potentials. Invest Ophthalmol Vis Sci 16:1073–1076Google Scholar
  2. 2.
    Bach M, Maurer JP, Wolf ME (2008) Visual evoked potential-based acuity assessment in normal vision, artificially degraded vision, and in patients. Br J Ophthalmol 92:396–403. CrossRefGoogle Scholar
  3. 3.
    Hoffmann MB, Brands J, Behrens-Baumann W, Bach M (2017) VEP-based acuity assessment in low vision. Doc Ophthalmol 135:209–218. CrossRefGoogle Scholar
  4. 4.
    Chan C, Smith G, Jacobs RJ (1985) Simulating refractive errors: source and observer methods. Am J Optom Physiol Opt 62:207–216CrossRefGoogle Scholar
  5. 5.
    Ohlendorf A, Tabernero J, Schaeffel F (2011) Neuronal adaptation to simulated and optically-induced astigmatic defocus. Vis Res 51:529–534. CrossRefGoogle Scholar
  6. 6.
    McAnany JJ, Shahidi M, Applegate RA et al (2011) Contributions of optical and non-optical blur to variation in visual acuity. Optom Vis Sci 88:716–723. CrossRefGoogle Scholar
  7. 7.
    Dehnert A, Bach M, Heinrich SP (2011) Subjective visual acuity with simulated defocus. Ophthalmic Physiol Opt 31:625–631. CrossRefGoogle Scholar
  8. 8.
    Heinrich SP, Bock CM, Bach M (2016) Imitating the effect of amblyopia on VEP-based acuity estimates. Doc Ophthalmol 133:183–187. CrossRefGoogle Scholar
  9. 9.
    Beusterien ML, Heinrich SP (2018) P300-based acuity estimation in imitated amblyopia. Doc Ophthalmol 136:69–74. CrossRefGoogle Scholar
  10. 10.
    Strasburger H, Bach M, Heinrich SP (2018) Blur unblurred—a mini tutorial. i-Perception 9:2041669518765850. Google Scholar
  11. 11.
    Heinrich SP, Lüth I, Bach M (2015) Event-related potentials allow for optotype-based objective acuity estimation. Invest Ophthalmol Vis Sci 56:2184–2191. CrossRefGoogle Scholar
  12. 12.
    Jägle H, Zobor D, Brauns T (2010) Accommodation limits induced optical defocus in defocus experiments. Doc Ophthalmol 121:103–109. CrossRefGoogle Scholar
  13. 13.
    de Wit GC, Franssen L, Coppens JE, van den Berg TJTP (2006) Simulating the straylight effects of cataracts. J Cataract Refract Surg 32:294–300. CrossRefGoogle Scholar
  14. 14.
    Stein RS, Rhodes MB (1960) Photographic light scattering by polyethylene films. J Appl Phys 31:1873–1884. CrossRefGoogle Scholar
  15. 15.
    Ward IM (2012) Structure and properties of oriented polymers, 2nd edn. Springer, BerlinGoogle Scholar
  16. 16.
    Ang A (n.d.) Predicting scatter of Light Shaping Diffuser® angles using Luminit’s proprietary optical model and OpticStudio. Luminit LLC. Accessed 02 Sept 2019
  17. 17.
    Odell NV, Leske DA, Hatt SR et al (2008) The effect of Bangerter filters on optotype acuity, vernier acuity, and contrast sensitivity. J AAPOS 12:555–559. CrossRefGoogle Scholar
  18. 18.
    Pérez GM, Archer SM, Artal P (2010) Optical characterization of Bangerter foils. Invest Ophthalmol Vis Sci 51:609–613. CrossRefGoogle Scholar
  19. 19.
    Goodman-Deane J, Waller S, Collins A-C, Clarkson PJ (2013) Simulating vision loss. Contemp Ergon Hum Factors 2013:347–354. Google Scholar
  20. 20.
    Bach M (1996) The Freiburg Visual Acuity Test–automatic measurement of visual acuity. Optom Vis Sci 73:49–53CrossRefGoogle Scholar
  21. 21.
    Hertenstein H, Bach M, Gross NJ, Beisse F (2016) Marked dissociation of photopic and mesopic contrast sensitivity even in normal observers. Graefes Arch Clin Exp Ophthalmol 254:373–384. CrossRefGoogle Scholar
  22. 22.
    Odom JV, Bach M, Brigell M et al (2016) ISCEV standard for clinical visual evoked potentials: (2016 update). Doc Ophthalmol 133:1–9. CrossRefGoogle Scholar
  23. 23.
    Chung STL, Legge GE (2016) Comparing the shape of contrast sensitivity functions for normal and low vision. Invest Ophthalmol Vis Sci 57:198–207. CrossRefGoogle Scholar
  24. 24.
    Heinrich SP, Bach M (2013) Resolution acuity versus recognition acuity with Landolt-style optotypes. Graefes Arch Clin Exp Ophthalmol 251:2235–2241. CrossRefGoogle Scholar
  25. 25.
    Paudel N, Jacobs RJ, Sloan R et al (2017) Effect of simulated refractive error on adult visual acuity for paediatric tests. Ophthalmic Physiol Opt 37:521–530. CrossRefGoogle Scholar
  26. 26.
    Poulere E, Moschandreas J, Kontadakis GA et al (2013) Effect of blur and subsequent adaptation on visual acuity using letter and Landolt C charts: differences between emmetropes and myopes. Ophthalmic Physiol Opt 33:130–137. CrossRefGoogle Scholar
  27. 27.
    Venkataraman AP, Winter S, Unsbo P, Lundström L (2015) Blur adaptation: contrast sensitivity changes and stimulus extent. Vis Res 110:100–106. CrossRefGoogle Scholar
  28. 28.
    Sokol S, Moskowitz A (1981) Effect of retinal blur on the peak latency of the pattern evoked potential. Vis Res 21:1279–1286CrossRefGoogle Scholar
  29. 29.
    Bobak P, Bodis-Wollner I, Guillory S (1987) The effect of blur and contrast on VEP latency: comparison between check and sinusoidal and grating patterns. Electroencephalogr Clin Neurophysiol 68:247–255CrossRefGoogle Scholar
  30. 30.
    Sireteanu R, Lagreze W-D, Constantinescu DH (1993) Distortions in two-dimensional visual space perception in strabismic observers. Vis Res 33:677–690. CrossRefGoogle Scholar
  31. 31.
    Barrett BT, Pacey IE, Bradley A et al (2003) Nonveridical visual perception in human amblyopia. Invest Ophthalmol Vis Sci 44:1555–1567. CrossRefGoogle Scholar
  32. 32.
    Elliott DB (1993) Evaluating visual function in cataract. Optom Vis Sci 70:896–902CrossRefGoogle Scholar
  33. 33.
    Artal P, Benito A, Pérez GM et al (2011) An objective scatter index based on double-pass retinal images of a point source to classify cataracts. PLoS ONE 6:e16823. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Eye Center, Medical CenterUniversity of FreiburgFreiburgGermany
  2. 2.Faculty of MedicineUniversity of FreiburgFreiburgGermany

Personalised recommendations