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Learning-Based Proof of the State-of-the-Art Geometric Hypothesis on Depth-of-Field Scaling and Shifting Influence on Image Sharpness
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.ORCID iD: 0000-0003-4327-117x
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.ORCID iD: 0000-0003-3887-5972
Ardakan University, Iran.ORCID iD: 0000-0002-8801-5017
2024 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 14, no 7, article id 2748Article in journal (Refereed) Published
Abstract [en]

Today, we capture and store images in a way that has never been possible. However, huge numbers of degraded and blurred images are captured unintentionally or by mistake. In this paper, we propose a geometrical hypothesis stating that blurring occurs by shifting or scaling the depth of field (DOF). The validity of the hypothesis is proved by an independent method based on depth estimation from a single image. The image depth is modeled regarding its edges to extract amplitude comparison ratios between the generated blurred images and the sharp/blurred images. Blurred images are generated by a stepwise variation in the standard deviation of the Gaussian filter estimate in the improved model. This process acts as virtual image recording used to mimic the recording of several image instances. A historical documentation database is used to validate the hypothesis and classify sharp images from blurred ones and different blur types. The experimental results show that distinguishing unintentionally blurred images from non-blurred ones by a comparison of their depth of field is applicable.

Place, publisher, year, edition, pages
MDPI, 2024. Vol. 14, no 7, article id 2748
Keywords [en]
unintentional blur, shifting, scaling, depth of field, blurred image
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-26144DOI: 10.3390/app14072748ISI: 001200913900001Scopus ID: 2-s2.0-85192573889OAI: oai:DiVA.org:bth-26144DiVA, id: diva2:1854389
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation
Funder
Knowledge Foundation, 20140032Available from: 2024-04-25 Created: 2024-04-25 Last updated: 2024-05-27Bibliographically approved

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Khatibi, SiamakWen, Wei

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