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Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm

With the continuous development and progress of image sensing technology in recent years, the problem of image data acquisition, recovery, and recording has been solved, and people have gradually realized that image data can be better recovered, recorded, and exhibited by collecting image data. It i...

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Autor principal: Wu, Man
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303091/
https://www.ncbi.nlm.nih.gov/pubmed/35875773
http://dx.doi.org/10.1155/2022/7663659
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author Wu, Man
author_facet Wu, Man
author_sort Wu, Man
collection PubMed
description With the continuous development and progress of image sensing technology in recent years, the problem of image data acquisition, recovery, and recording has been solved, and people have gradually realized that image data can be better recovered, recorded, and exhibited by collecting image data. It is also used in target recognition, image display, landscape design, and other fields. And the application of color mapping algorithm can more intuitively affect the final quality of the display image. This is extremely important for landscape design. It can be said that the emergence and application of color mapping algorithm provide strong technical support for both dynamic and static image color automatic adjustment and simulation. On this basis, taking the color mapping algorithm as the breakthrough, through the in-depth introduction of the color mapping algorithm, starting from the direction of landscape image color automatic adjustment, a relatively simplified color mapping algorithm model inheriting the positive and negative two-way vision model is proposed. At the same time, a simulation algorithm based on landscape suitcase color automatic adjustment is proposed based on the color mapping algorithm model. Experiments show that automatic color adjustment of landscape images based on color mapping algorithm can achieve more realistic image reproduction, and the color mapping method based on color mapping algorithm has less illumination bias. When the number of experiments is 30, the difference in visibility of the plane is 0.25 cd/m(2), the difference of visibility according to the color change segmented by the gradient area is 0.71 cd/m(2), and the difference of the illuminance difference of the transition image is 0.71 cd/m(2). Gaussian pyramid is 1.25 cd/m(2). The proposed method improves color density by reducing image sharpness and compensating for color sharpness of landscape image, and high quality landscape images provide assistance for landscape design.
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spelling pubmed-93030912022-07-22 Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm Wu, Man Comput Intell Neurosci Research Article With the continuous development and progress of image sensing technology in recent years, the problem of image data acquisition, recovery, and recording has been solved, and people have gradually realized that image data can be better recovered, recorded, and exhibited by collecting image data. It is also used in target recognition, image display, landscape design, and other fields. And the application of color mapping algorithm can more intuitively affect the final quality of the display image. This is extremely important for landscape design. It can be said that the emergence and application of color mapping algorithm provide strong technical support for both dynamic and static image color automatic adjustment and simulation. On this basis, taking the color mapping algorithm as the breakthrough, through the in-depth introduction of the color mapping algorithm, starting from the direction of landscape image color automatic adjustment, a relatively simplified color mapping algorithm model inheriting the positive and negative two-way vision model is proposed. At the same time, a simulation algorithm based on landscape suitcase color automatic adjustment is proposed based on the color mapping algorithm model. Experiments show that automatic color adjustment of landscape images based on color mapping algorithm can achieve more realistic image reproduction, and the color mapping method based on color mapping algorithm has less illumination bias. When the number of experiments is 30, the difference in visibility of the plane is 0.25 cd/m(2), the difference of visibility according to the color change segmented by the gradient area is 0.71 cd/m(2), and the difference of the illuminance difference of the transition image is 0.71 cd/m(2). Gaussian pyramid is 1.25 cd/m(2). The proposed method improves color density by reducing image sharpness and compensating for color sharpness of landscape image, and high quality landscape images provide assistance for landscape design. Hindawi 2022-07-14 /pmc/articles/PMC9303091/ /pubmed/35875773 http://dx.doi.org/10.1155/2022/7663659 Text en Copyright © 2022 Man Wu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Man
Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm
title Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm
title_full Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm
title_fullStr Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm
title_full_unstemmed Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm
title_short Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm
title_sort simulation of automatic color adjustment of landscape image based on color mapping algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303091/
https://www.ncbi.nlm.nih.gov/pubmed/35875773
http://dx.doi.org/10.1155/2022/7663659
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