Zheng-Jie Huang, Wei-Hao Lu, Brijesh Patel, Po-Yan Chiu, Tz-Yu Yang, Hao Jian Tong, Vytautas Bučinskas, Modris Greitans, Po Ting Lin. Convolutional Neural Network-based Image Restoration (CNNIR). 2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), IEEE, 2022.
Bibtex citāts:
Bibtex citāts:
@inproceedings{13759_2022,
author = {Zheng-Jie Huang and Wei-Hao Lu and Brijesh Patel and Po-Yan Chiu and Tz-Yu Yang and Hao Jian Tong and Vytautas Bučinskas and Modris Greitans and Po Ting Lin},
title = {Convolutional Neural Network-based Image Restoration (CNNIR)},
journal = {2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)},
publisher = {IEEE},
year = {2022}
}
author = {Zheng-Jie Huang and Wei-Hao Lu and Brijesh Patel and Po-Yan Chiu and Tz-Yu Yang and Hao Jian Tong and Vytautas Bučinskas and Modris Greitans and Po Ting Lin},
title = {Convolutional Neural Network-based Image Restoration (CNNIR)},
journal = {2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)},
publisher = {IEEE},
year = {2022}
}
Anotācija: In this era of automation, image processing is an indispensable part of computer vision. Many computer vision approaches in the industry depend on a relatively bright environment. Under low light source conditions, the distribution of image information is too concentrated in specific intensity ranges due to the color factor of the subject itself, resulting in noise and contrast loss. Enhancing contrast is a crucial step in improving the quality of the image and showing visible details. This study proposes a method based on a convolutional neural network (CNN), using the pixel difference between paired images, called a motion matrix, as an annotation for low-contrast images. The image's motion vector is predicted after the neural network model has been trained to produce the low-contrast enhanced image. Then, the proposed model is compared with the other methods.