Visual and Physiological Optics: Optical Design, Image Processing and Machine Learning Algorithms

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1526

Special Issue Editor


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Guest Editor
Department of Applied Physics, University of Zaragoza, Zaragoza, Spain
Interests: visual optics; optical design; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The visual function is the neural processing of the spatiotemporal information that occurs at the retina after light–matter interaction through the optics of the eye. Then, a proper decoding of the visual information requires both good retinal image quality of the eye as an optical instrument and good visual perception processes.

Therefore, the assessment of the visual function must be carried-out in terms of pure optical quality measurements and psychophysical approaches. Whereas optical assessment does not require a patient response, the visual perception must be evaluated via interacting with the subject.

On the other hand, the assessment of the ocular structures such as the cornea, crystalline lens and the retina needs high-resolution and high-speed acquisition imaging systems that usually require image processing algorithms to first improve the spatiotemporal information and then to extract semantic information from the clinical images. It is a constant challenge to develop optimized imaging instruments that are complemented with machine learning algorithms both in the processing of large datasets and in the extraction of relevant information through computer vision algorithms.

We invite you to participate in this Special Issue involving the optics of the eye, psychophysical aspects of the visual perception, new optical designs and the development of machine learning algorithms for image processing in visual and physiological optics.  

Dr. Francisco Javier Avila Gomez
Guest Editor

Manuscript Submission Information

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Keywords

  • visual optics
  • physiological optics
  • visual perception
  • optical design
  • optics of the eye
  • machine learning algorithms
  • image processing

Published Papers (1 paper)

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Research

14 pages, 8556 KiB  
Article
Spherical Aberration and Scattering Compensation in Microscopy Images through a Blind Deconvolution Method
by Francisco J. Ávila and Juan M. Bueno
J. Imaging 2024, 10(2), 43; https://doi.org/10.3390/jimaging10020043 - 07 Feb 2024
Viewed by 1173
Abstract
The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium the light passes while travelling from the object to the image plane. The computation of the point spread function (PSF) [...] Read more.
The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium the light passes while travelling from the object to the image plane. The computation of the point spread function (PSF) associated to the optical system is often used to assess the image quality. In a non-ideal optical system, the PSF is affected by aberrations that distort the final image. Moreover, in the presence of turbid media, the scattering phenomena spread the light at wide angular distributions that contribute to reduce contrast and sharpness. If the mathematical degradation operator affecting the recorded image is known, the image can be restored through deconvolution methods. In some scenarios, no (or partial) information on the PSF is available. In those cases, blind deconvolution approaches arise as useful solutions for image restoration. In this work, a new blind deconvolution method is proposed to restore images using spherical aberration (SA) and scatter-based kernel filters. The procedure was evaluated in different microscopy images. The results show the capability of the algorithm to detect both degradation coefficients (i.e., SA and scattering) and to restore images without information on the real PSF. Full article
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