{"title":"用于机器视觉图像增强的光谱计算模型","authors":"Rui Bao , Wanlu Zhang , Ruiqian Guo","doi":"10.1016/j.optlastec.2024.111806","DOIUrl":null,"url":null,"abstract":"<div><p>With the development of artificial intelligence technology, the demand for image quality in machine vision systems is increasing. However, current research mainly focuses on chromaticity and light environment indicators, and cannot fundamentally solve the problem. To solve this problem, we established a machine vision optimal spectral calculation model from the perspective of physical energy, designed a narrowband spectral experiment, and analyzed it using JS divergence. The results showed that the calculated optimal spectrum significantly improved the image brightness and JS divergence compared to Standard White, with a maximum increase of 135.66% in image brightness and 82% in JS divergence. Research has found a significant linear correlation between the brightness value of machine vision images and the irradiance with a coefficient of 1, but not with the illumination. It was also found that the divergence of JS is not related to the irradiance, but has a significant linear correlation with the difference in spectral distribution with a coefficient of 1. These findings will provide a new basis and ideas for the light environment design of machine vision systems, provide new methods for improving system image quality, and have a significant positive impact on deep learning of the machine vision system.</p></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"181 ","pages":"Article 111806"},"PeriodicalIF":4.6000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spectral calculation model for machine vision image enhancement\",\"authors\":\"Rui Bao , Wanlu Zhang , Ruiqian Guo\",\"doi\":\"10.1016/j.optlastec.2024.111806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the development of artificial intelligence technology, the demand for image quality in machine vision systems is increasing. However, current research mainly focuses on chromaticity and light environment indicators, and cannot fundamentally solve the problem. To solve this problem, we established a machine vision optimal spectral calculation model from the perspective of physical energy, designed a narrowband spectral experiment, and analyzed it using JS divergence. The results showed that the calculated optimal spectrum significantly improved the image brightness and JS divergence compared to Standard White, with a maximum increase of 135.66% in image brightness and 82% in JS divergence. Research has found a significant linear correlation between the brightness value of machine vision images and the irradiance with a coefficient of 1, but not with the illumination. It was also found that the divergence of JS is not related to the irradiance, but has a significant linear correlation with the difference in spectral distribution with a coefficient of 1. These findings will provide a new basis and ideas for the light environment design of machine vision systems, provide new methods for improving system image quality, and have a significant positive impact on deep learning of the machine vision system.</p></div>\",\"PeriodicalId\":19511,\"journal\":{\"name\":\"Optics and Laser Technology\",\"volume\":\"181 \",\"pages\":\"Article 111806\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optics and Laser Technology\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0030399224012647\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics and Laser Technology","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030399224012647","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
Spectral calculation model for machine vision image enhancement
With the development of artificial intelligence technology, the demand for image quality in machine vision systems is increasing. However, current research mainly focuses on chromaticity and light environment indicators, and cannot fundamentally solve the problem. To solve this problem, we established a machine vision optimal spectral calculation model from the perspective of physical energy, designed a narrowband spectral experiment, and analyzed it using JS divergence. The results showed that the calculated optimal spectrum significantly improved the image brightness and JS divergence compared to Standard White, with a maximum increase of 135.66% in image brightness and 82% in JS divergence. Research has found a significant linear correlation between the brightness value of machine vision images and the irradiance with a coefficient of 1, but not with the illumination. It was also found that the divergence of JS is not related to the irradiance, but has a significant linear correlation with the difference in spectral distribution with a coefficient of 1. These findings will provide a new basis and ideas for the light environment design of machine vision systems, provide new methods for improving system image quality, and have a significant positive impact on deep learning of the machine vision system.
期刊介绍:
Optics & Laser Technology aims to provide a vehicle for the publication of a broad range of high quality research and review papers in those fields of scientific and engineering research appertaining to the development and application of the technology of optics and lasers. Papers describing original work in these areas are submitted to rigorous refereeing prior to acceptance for publication.
The scope of Optics & Laser Technology encompasses, but is not restricted to, the following areas:
•development in all types of lasers
•developments in optoelectronic devices and photonics
•developments in new photonics and optical concepts
•developments in conventional optics, optical instruments and components
•techniques of optical metrology, including interferometry and optical fibre sensors
•LIDAR and other non-contact optical measurement techniques, including optical methods in heat and fluid flow
•applications of lasers to materials processing, optical NDT display (including holography) and optical communication
•research and development in the field of laser safety including studies of hazards resulting from the applications of lasers (laser safety, hazards of laser fume)
•developments in optical computing and optical information processing
•developments in new optical materials
•developments in new optical characterization methods and techniques
•developments in quantum optics
•developments in light assisted micro and nanofabrication methods and techniques
•developments in nanophotonics and biophotonics
•developments in imaging processing and systems