The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Peningkatan imej memainkan peranan penting dalam banyak aplikasi penglihatan mesin pada imej yang ditangkap dalam keadaan kontras rendah dan pencahayaan rendah. Dalam kajian ini, kami mencadangkan kaedah baharu untuk peningkatan imej berdasarkan analisis pada permukaan terbenam imej. Kaedah yang dicadangkan memberi gambaran tentang hubungan antara keamatan imej dan peningkatan imej. Dalam kaedah kami, luas permukaan berskala dan volum permukaan dicadangkan dan digunakan untuk membina semula imej secara berulang untuk peningkatan kontras, dan pencahayaan imej yang dibina semula juga boleh dilaraskan secara serentak. Sebaliknya, kaedah yang paling biasa untuk mengukur kualiti imej yang dipertingkatkan ialah Mean Square Error (MSE) atau Peak Signal-to-Noise-Nisbah (PSNR) dalam kerja konvensional. Kedua-dua langkah itu telah diiktiraf sebagai langkah yang tidak mencukupi kerana ia tidak menilai keputusan seperti yang dilakukan oleh sistem penglihatan manusia. Kertas kerja ini juga membentangkan rangka kerja baharu untuk menilai peningkatan imej menggunakan kedua-dua ukuran objektif dan subjektif. Rangka kerja ini juga boleh digunakan untuk penilaian kualiti imej lain seperti penilaian denoising. Kami membandingkan kaedah peningkatan kami dengan beberapa algoritma peningkatan yang terkenal, termasuk kaedah wavelet dan curvelet, menggunakan rangka kerja penilaian baharu. Keputusan menunjukkan bahawa kaedah kami boleh memberikan prestasi yang lebih baik dalam kebanyakan kriteria objektif dan subjektif daripada kaedah konvensional.
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Salinan
Li TIAN, Sei-ichiro KAMATA, "Image Enhancement by Analysis on Embedded Surfaces of Images and a New Framework for Enhancement Evaluation" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 1946-1954, July 2008, doi: 10.1093/ietisy/e91-d.7.1946.
Abstract: Image enhancement plays an important role in many machine vision applications on images captured in low contrast and low illumination conditions. In this study, we propose a new method for image enhancement based on analysis on embedded surfaces of images. The proposed method gives an insight into the relationship between the image intensity and image enhancement. In our method, scaled surface area and the surface volume are proposed and used to reconstruct the image iteratively for contrast enhancement, and the illumination of the reconstructed image can also be adjusted simultaneously. On the other hand, the most common methods for measuring the quality of enhanced images are Mean Square Error (MSE) or Peak Signal-to-Noise-Ratio (PSNR) in conventional works. The two measures have been recognized as inadequate ones because they do not evaluate the result in the way that the human vision system does. This paper also presents a new framework for evaluating image enhancement using both objective and subjective measures. This framework can also be used for other image quality evaluations such as denoising evaluation. We compare our enhancement method with some well-known enhancement algorithms, including wavelet and curvelet methods, using the new evaluation framework. The results show that our method can give better performance in most objective and subjective criteria than the conventional methods.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.1946/_p
Salinan
@ARTICLE{e91-d_7_1946,
author={Li TIAN, Sei-ichiro KAMATA, },
journal={IEICE TRANSACTIONS on Information},
title={Image Enhancement by Analysis on Embedded Surfaces of Images and a New Framework for Enhancement Evaluation},
year={2008},
volume={E91-D},
number={7},
pages={1946-1954},
abstract={Image enhancement plays an important role in many machine vision applications on images captured in low contrast and low illumination conditions. In this study, we propose a new method for image enhancement based on analysis on embedded surfaces of images. The proposed method gives an insight into the relationship between the image intensity and image enhancement. In our method, scaled surface area and the surface volume are proposed and used to reconstruct the image iteratively for contrast enhancement, and the illumination of the reconstructed image can also be adjusted simultaneously. On the other hand, the most common methods for measuring the quality of enhanced images are Mean Square Error (MSE) or Peak Signal-to-Noise-Ratio (PSNR) in conventional works. The two measures have been recognized as inadequate ones because they do not evaluate the result in the way that the human vision system does. This paper also presents a new framework for evaluating image enhancement using both objective and subjective measures. This framework can also be used for other image quality evaluations such as denoising evaluation. We compare our enhancement method with some well-known enhancement algorithms, including wavelet and curvelet methods, using the new evaluation framework. The results show that our method can give better performance in most objective and subjective criteria than the conventional methods.},
keywords={},
doi={10.1093/ietisy/e91-d.7.1946},
ISSN={1745-1361},
month={July},}
Salinan
TY - JOUR
TI - Image Enhancement by Analysis on Embedded Surfaces of Images and a New Framework for Enhancement Evaluation
T2 - IEICE TRANSACTIONS on Information
SP - 1946
EP - 1954
AU - Li TIAN
AU - Sei-ichiro KAMATA
PY - 2008
DO - 10.1093/ietisy/e91-d.7.1946
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - Image enhancement plays an important role in many machine vision applications on images captured in low contrast and low illumination conditions. In this study, we propose a new method for image enhancement based on analysis on embedded surfaces of images. The proposed method gives an insight into the relationship between the image intensity and image enhancement. In our method, scaled surface area and the surface volume are proposed and used to reconstruct the image iteratively for contrast enhancement, and the illumination of the reconstructed image can also be adjusted simultaneously. On the other hand, the most common methods for measuring the quality of enhanced images are Mean Square Error (MSE) or Peak Signal-to-Noise-Ratio (PSNR) in conventional works. The two measures have been recognized as inadequate ones because they do not evaluate the result in the way that the human vision system does. This paper also presents a new framework for evaluating image enhancement using both objective and subjective measures. This framework can also be used for other image quality evaluations such as denoising evaluation. We compare our enhancement method with some well-known enhancement algorithms, including wavelet and curvelet methods, using the new evaluation framework. The results show that our method can give better performance in most objective and subjective criteria than the conventional methods.
ER -