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Histogram equalization using a selective filter
Many popular modern image processing software packages implement a naïve form of histogram equalization. This implementation is known to produce histograms that are not truly uniform. While exact histogram equalization techniques exist, these may produce undesirable artifacts in some scenarios. In t...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635952/ https://www.ncbi.nlm.nih.gov/pubmed/37969935 http://dx.doi.org/10.1007/s00371-022-02723-8 |
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author | Dyke, Roberto M. Hormann, Kai |
author_facet | Dyke, Roberto M. Hormann, Kai |
author_sort | Dyke, Roberto M. |
collection | PubMed |
description | Many popular modern image processing software packages implement a naïve form of histogram equalization. This implementation is known to produce histograms that are not truly uniform. While exact histogram equalization techniques exist, these may produce undesirable artifacts in some scenarios. In this paper we consider the link between the established continuous theory for global histogram equalization and its discrete implementation, and we formulate a novel histogram equalization technique that builds upon and considerably improves the naïve approach. We show that we can linearly interpolate the cumulative distribution of a low-bit image by approximately dequantizing its intensities using a selective box filter. This helps to distribute the intensities more evenly. The proposed algorithm is subsequently evaluated and compared with existing works in the literature. We find that the method is capable of producing an equalized histogram that has a high entropy, while distances between similar intensities are preserved. The described approach has implications on several related image processing problems, e.g., edge detection. |
format | Online Article Text |
id | pubmed-10635952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-106359522023-11-14 Histogram equalization using a selective filter Dyke, Roberto M. Hormann, Kai Vis Comput Original Article Many popular modern image processing software packages implement a naïve form of histogram equalization. This implementation is known to produce histograms that are not truly uniform. While exact histogram equalization techniques exist, these may produce undesirable artifacts in some scenarios. In this paper we consider the link between the established continuous theory for global histogram equalization and its discrete implementation, and we formulate a novel histogram equalization technique that builds upon and considerably improves the naïve approach. We show that we can linearly interpolate the cumulative distribution of a low-bit image by approximately dequantizing its intensities using a selective box filter. This helps to distribute the intensities more evenly. The proposed algorithm is subsequently evaluated and compared with existing works in the literature. We find that the method is capable of producing an equalized histogram that has a high entropy, while distances between similar intensities are preserved. The described approach has implications on several related image processing problems, e.g., edge detection. Springer Berlin Heidelberg 2022-11-29 2023 /pmc/articles/PMC10635952/ /pubmed/37969935 http://dx.doi.org/10.1007/s00371-022-02723-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Dyke, Roberto M. Hormann, Kai Histogram equalization using a selective filter |
title | Histogram equalization using a selective filter |
title_full | Histogram equalization using a selective filter |
title_fullStr | Histogram equalization using a selective filter |
title_full_unstemmed | Histogram equalization using a selective filter |
title_short | Histogram equalization using a selective filter |
title_sort | histogram equalization using a selective filter |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635952/ https://www.ncbi.nlm.nih.gov/pubmed/37969935 http://dx.doi.org/10.1007/s00371-022-02723-8 |
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