Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training
Numerous studies are underway to enhance the identification of surroundings in nighttime environments. These studies explore methods such as utilizing infrared images to improve night image visibility or converting night images into day-like representations for enhanced visibility. This research pre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/14/3102 |
| _version_ | 1797588414970724352 |
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| author | Dong-Min Son Hyuk-Ju Kwon Sung-Hak Lee |
| author_facet | Dong-Min Son Hyuk-Ju Kwon Sung-Hak Lee |
| author_sort | Dong-Min Son |
| collection | DOAJ |
| description | Numerous studies are underway to enhance the identification of surroundings in nighttime environments. These studies explore methods such as utilizing infrared images to improve night image visibility or converting night images into day-like representations for enhanced visibility. This research presents a technique focused on converting the road conditions depicted in night images to resemble daytime scenes. To facilitate this, a paired dataset is created by augmenting limited day and night image data using CycleGAN. The model is trained using both original night images and single-scale luminance transform (SLAT) day images to enhance the level of detail in the converted daytime images. However, the generated daytime images may exhibit sharpness and noise issues. To address these concerns, an image processing approach, inspired by the Stevens effect and local blurring, which align with visual characteristics, is employed to reduce noise and enhance image details. Consequently, this study contributes to improving the visibility of night images by means of day image conversion and subsequent image processing. The proposed night-to-day image translation in this study has a processing time of 0.81 s, including image processing, which is less than one second. Therefore, it is considered valuable as a module for daytime image translation. Additionally, the image quality assessment metric, BRISQUE, yielded a score of 19.8, indicating better performance compared to conventional methods. The outcomes of this research hold potential applications in fields such as CCTV surveillance systems and self-driving cars. |
| first_indexed | 2024-03-11T00:51:38Z |
| format | Article |
| id | doaj.art-42f25340a87a428eb2cc36a7248493f5 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:51:38Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-42f25340a87a428eb2cc36a7248493f52023-11-18T20:20:35ZengMDPI AGMathematics2227-73902023-07-011114310210.3390/math11143102Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired TrainingDong-Min Son0Hyuk-Ju Kwon1Sung-Hak Lee2School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of KoreaNumerous studies are underway to enhance the identification of surroundings in nighttime environments. These studies explore methods such as utilizing infrared images to improve night image visibility or converting night images into day-like representations for enhanced visibility. This research presents a technique focused on converting the road conditions depicted in night images to resemble daytime scenes. To facilitate this, a paired dataset is created by augmenting limited day and night image data using CycleGAN. The model is trained using both original night images and single-scale luminance transform (SLAT) day images to enhance the level of detail in the converted daytime images. However, the generated daytime images may exhibit sharpness and noise issues. To address these concerns, an image processing approach, inspired by the Stevens effect and local blurring, which align with visual characteristics, is employed to reduce noise and enhance image details. Consequently, this study contributes to improving the visibility of night images by means of day image conversion and subsequent image processing. The proposed night-to-day image translation in this study has a processing time of 0.81 s, including image processing, which is less than one second. Therefore, it is considered valuable as a module for daytime image translation. Additionally, the image quality assessment metric, BRISQUE, yielded a score of 19.8, indicating better performance compared to conventional methods. The outcomes of this research hold potential applications in fields such as CCTV surveillance systems and self-driving cars.https://www.mdpi.com/2227-7390/11/14/3102image-to-image translationCycleGANPix2Pixluminance adaptation transformimage processingStevens effect |
| spellingShingle | Dong-Min Son Hyuk-Ju Kwon Sung-Hak Lee Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training Mathematics image-to-image translation CycleGAN Pix2Pix luminance adaptation transform image processing Stevens effect |
| title | Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training |
| title_full | Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training |
| title_fullStr | Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training |
| title_full_unstemmed | Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training |
| title_short | Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training |
| title_sort | enhanced night to day image conversion using cyclegan based base detail paired training |
| topic | image-to-image translation CycleGAN Pix2Pix luminance adaptation transform image processing Stevens effect |
| url | https://www.mdpi.com/2227-7390/11/14/3102 |
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