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OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution
There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672420/ https://www.ncbi.nlm.nih.gov/pubmed/37998093 http://dx.doi.org/10.3390/jimaging9110246 |
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author | Bhardwaj, Rishav Jothi Balaji, Janarthanam Lakshminarayanan, Vasudevan |
author_facet | Bhardwaj, Rishav Jothi Balaji, Janarthanam Lakshminarayanan, Vasudevan |
author_sort | Bhardwaj, Rishav |
collection | PubMed |
description | There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images. |
format | Online Article Text |
id | pubmed-10672420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106724202023-11-08 OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution Bhardwaj, Rishav Jothi Balaji, Janarthanam Lakshminarayanan, Vasudevan J Imaging Article There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images. MDPI 2023-11-08 /pmc/articles/PMC10672420/ /pubmed/37998093 http://dx.doi.org/10.3390/jimaging9110246 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bhardwaj, Rishav Jothi Balaji, Janarthanam Lakshminarayanan, Vasudevan OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution |
title | OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution |
title_full | OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution |
title_fullStr | OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution |
title_full_unstemmed | OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution |
title_short | OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution |
title_sort | ow-slr: overlapping windows on semi-local region for image super-resolution |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672420/ https://www.ncbi.nlm.nih.gov/pubmed/37998093 http://dx.doi.org/10.3390/jimaging9110246 |
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