Special Issue "Blind Image Restoration"

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

Deadline for manuscript submissions: 20 September 2021.

Special Issue Editor

Dr. Yitzhak Yitzhaky
E-Mail Website
Guest Editor
Department of Electro-Optics Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
Interests: computer vision; image restoration and enhancement; image processing for vision rehabilitation; imaging through the atmosphere; 3D imaging

Special Issue Information

Dear Colleagues,

Blind image restoration aims to recover an image from its blurred observation without prior knowledge of the blur mathematical description.

While common imaging systems continue to gain higher quality and image resolution, sources of blur degradation such as motion, diffusing imaging mediums, de-focus, imperfect system components, and others still exist and may be even more significant with a higher imaging resolution. In most real-life blur causes, only a limited prior knowledge exists regarding blur characteristics, and thus, some kind of blind image restoration is required. Some of the methods approach this challenge by first estimating the blur, and then use it to restore the latent sharp image, while others perform both operations iteratively.

Recently, with the advance of powerful computing and machine learning via deep neural networks, learning-based methods have been developed, which, unlike most previous approaches, inherently do not necessarily require space-invariant and linear image blurring models. Although these new developments promise a more flexible and efficient blur removal, challenges in blind image restoration in various complicated scenarios are yet to be addressed.

Applications of blind image restoration cover a large variety of disciplines, including medical, industrial, and security.

This Special Issue wishes to gather diverse and complementary contributions that demonstrate novel developments, techniques, and applications that will further advance the field of blind image restoration.

Dr. Yitzhak Yitzhaky
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image degradations
  • spatially-varying blur
  • ill-posed problems
  • blur estimation
  • blind deconvolution
  • inverse problems
  • motion blur
  • imaging through diffusive medium
  • imaging through the atmosphere

Published Papers (1 paper)

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Research

Article
Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images
J. Imaging 2021, 7(4), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7040073 - 16 Apr 2021
Viewed by 410
Abstract
The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium in which the light travels from the object to the final imaging sensor. The analysis of the point spread function [...] 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 in which the light travels from the object to the final imaging sensor. The analysis of the point spread function of the optical system is an objective way to quantify the image degradation. In retinal imaging, the presence of corneal or cristalline lens opacifications spread the light at wide angular distributions. If the mathematical operator that degrades the image is known, the image can be restored through deconvolution methods. In the particular case of retinal imaging, this operator may be unknown (or partially) due to the presence of cataracts, corneal edema, or vitreous opacification. In those cases, blind deconvolution theory provides useful results to restore important spatial information of the image. In this work, a new semi-blind deconvolution method has been developed by training an iterative process with the Glare Spread Function kernel based on the Richardson-Lucy deconvolution algorithm to compensate a veiling glare effect in retinal images due to intraocular straylight. The method was first tested with simulated retinal images generated from a straylight eye model and applied to a real retinal image dataset composed of healthy subjects and patients with glaucoma and diabetic retinopathy. Results showed the capacity of the algorithm to detect and compensate the veiling glare degradation and improving the image sharpness up to 1000% in the case of healthy subjects and up to 700% in the pathological retinal images. This image quality improvement allows performing image segmentation processing with restored hidden spatial information after deconvolution. Full article
(This article belongs to the Special Issue Blind Image Restoration)
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