Academic Journal of Computing & Information Science, 2023, 6(2); doi: 10.25236/AJCIS.2023.060214.
Dengjie Deng1, Jiahui Yang2
1Chongqing Metropolitan College of Science and Technology, Chongqing, 402160, China
2College of Information Science and Engineering, Shandong Agricultural University, Taian, China, 271001
Image acquisition system in the process of image acquisition, due to the influence of various uncontrollable factors, especially under adverse conditions such as indoor lighting and individual cases, image acquisition system often has a low contrast ratio, low dynamic range intensity, dark and bright areas of the image details disappear and other defects. Therefore, it has become a problem to obtain a clear image under the condition of finding the function. In this paper, Deep Lightening Network technology is used to analyze eye images under low-light conditions. Different from traditional methods, this method introduces machine learning method to generate enhanced images using learning models. Through experimental verification and comparison, the proposed algorithm can improve the overall brightness and contrast of the image. Based on 485 eye images, compared with two conventional lighten methods, the average PSNR score was 18.2420dB, and the SSIM score was 0.8011. Compared with the second best method, the PSNR and SSIM are improved by 2.02 and 0.01 respectively. Reduce the influence of uneven illumination, improve image quality and sharpness.
Image processing; Low-light image enhancement; Convolutional neural network; Deep learning
Dengjie Deng, Jiahui Yang. Research on Low-Light Enhancement of Eye Image Dataset Based on DLN. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 2: 104-108. https://doi.org/10.25236/AJCIS.2023.060214.
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