September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Effect of noise reduction in virtually structured detection based super-resolution Scanning Laser Microscopy
Author Affiliations & Notes
  • Minhaj Nur Alam
    Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
  • Damber Thapa
    Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
  • Yanan Zhi
    Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
  • Benquan Wang
    Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
  • Xincheng Yao
    Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
    Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, United States
  • Footnotes
    Commercial Relationships   Minhaj Nur Alam, None; Damber Thapa, None; Yanan Zhi, University of Illinois at Chicago (P); Benquan Wang, None; Xincheng Yao, University of Illinois at Chicago (P)
  • Footnotes
    Support  NIH R01 EY023522, NIH R01 EY024628, NIH P30 EY001792 and NSF CBET-1055889
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 5973. doi:
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    • Get Citation

      Minhaj Nur Alam, Damber Thapa, Yanan Zhi, Benquan Wang, Xincheng Yao; Effect of noise reduction in virtually structured detection based super-resolution Scanning Laser Microscopy. Invest. Ophthalmol. Vis. Sci. 2016;57(12):5973.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Virtually structured detection (VSD) is an easy, low-cost and phase-artifact free strategy to achieve super-resolution in scanning laser microscopy (SLM). However, in vivo application of this method for high speed imaging of retinal structures is still challenging because of inevitable noises due to illumination power limit allowed for safety concern. The purpose of this study is to explore denoising techniques to enhance the quality of the super-resolution image.

Methods : Freshly enucleated frog (Rana Pipiens) retina and standard optical target (USAF 1951 1X, EDMOND) were employed to examine the effect of noise reduction in VSD based super-resolution SLM. The super-resolution SLM system employed a 5X objective lens with numeric aperture (NA) 0.1. SLM images were captured with a 300 μW illumination power first. In order to demonstrate the noise effect on the SLM image, the power of the illumination light was reduced to 0.15 μW to get the nosier images. The stacks of 2D light profiles (raw images) obtained from SLM were denoised using a Wiener filter followed by a median filter, and then the noise suppressed 2D light profiles were used to reconstruct a super-resolution image using VSD technique. The super-resolution images with and without denoising involvements were quantitatively compared by calculating the noise level (i.e. standard deviation of noise).

Results : Figure 1a shows frog retinal image reconstructed with VSD and 1b shows denoising followed by VSD techniques. The results show that the use of noise reduction methods before VSD improved visual quality of the SLM image. The noise level reduced from 0.56 to 0.45 by implementing denoising process before the VSD reconstruction. The denoising effect is more prominent when the low illumination power was used for imaging acquisition. The noise level decreased from 8.70 to 2.48 by adding noise reduction techniques before VSD reconstruction in standard optical target.

Conclusions : Our experiments show noticeable improvement in the quality of super-resolutions images by implementing denoising techniques in the data processing step before the VSD reconstruction. Further improvement of the VSD method and denoising technique may provide a practical approach to achieve in vivo super-resolution imaging of retinal structures.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Reconstruction a) VSD, b) Denoising+VSD

Reconstruction a) VSD, b) Denoising+VSD

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