Image Quality Restoration on Historical Artifacts Using Histogram Equalization and Contrast Stretching Methods

This research was carried out to improve the image quality of historical artifacts that experience quality degradation such as noise, unclear or dark image contrast, and poor sharpness. Contrast Stretching and Histogram Equalization are image quality improvement methods to expand and even out image grayscale. This research aims to build a system for improving image quality on historical artifacts using contrast stretching and histogram equalization methods. The sample used in this research is RGB image data with 10 test images in .jpg format, while the process uses the Matlab programming language. The results of improving image quality using the contrast stretching and histogram equalization methods from sample 1 to image sample 10 showed the best results in test image_citra_1 with contrast stretching results based on PSNR = 32.61 dB and MSE = 35.86 Db and PSNR = 19.13 Db and MSE = 794.76 in histogram equalization calculations.

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Publication Details

Journal
Journal La Multiapp
Published
2024-08-05
DOI
https://doi.org/10.37899/journallamultiapp.v5i4.1442
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Image Quality Restoration on Historical Artifacts Using Histogram Equalization and Contrast Stretching Methods

Sriani Sriani, Yunda Aidilia
Journal La Multiapp
Industrial Vision Systems and Defect Detection
article

Image Quality Restoration on Historical Artifacts Using Histogram Equalization and Contrast Stretching Methods

Sriani Sriani, Yunda Aidilia
article en

Abstract

This research was carried out to improve the image quality of historical artifacts that experience quality degradation such as noise, unclear or dark image contrast, and poor sharpness. Contrast Stretching and Histogram Equalization are image quality improvement methods to expand and even out image grayscale. This research aims to build a system for improving image quality on historical artifacts using contrast stretching and histogram equalization methods. The sample used in this research is RGB image data with 10 test images in .jpg format, while the process uses the Matlab programming language. The results of improving image quality using the contrast stretching and histogram equalization methods from sample 1 to image sample 10 showed the best results in test image_citra_1 with contrast stretching results based on PSNR = 32.61 dB and MSE = 35.86 Db and PSNR = 19.13 Db and MSE = 794.76 in histogram equalization calculations.

Journal La MultiappVol. 5(4)
Universitas Islam Negeri Sumatera Utara (ID)
Openalex Percentile: Top 83%
Industrial Vision Systems and Defect Detection
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Image Quality Restoration on Historical Artifacts Using Histogram Equalization and Contrast Stretching Methods — Sriani Sriani, Yunda Aidilia · Journal La Multiapp (2024) | TGRS Research Map | TGRS