基于深度学习的低照度图像增强程序客户关系

本文翻译自英文原文，如有错误请指正。

摘要

低照度条件下获取的图像常常存在噪声、模糊和低对比度等问题，这会影响图像的质量和可视化效果。为了解决这些问题，本文提出了一种基于深度学习的低照度图像增强程序。该程序使用卷积神经网络（CNN）进行训练，以学习低照度图像的特征和相应的增强方法。通过对大量的低照度图像进行训练，该程序可以对新的低照度图像进行增强，从而提高图像的质量和可视化效果。

本文还介绍了该程序的客户关系，包括用户需求、用户反馈和用户体验。用户需求部分包括对低照度图像增强程序的功能和性能的需求。用户反馈部分包括对程序的评价和建议，以便改进和优化程序。用户体验部分包括程序的易用性、可靠性和效率等方面的评价。

最后，本文通过实验验证了该程序的有效性和可靠性。实验结果表明，该程序可以显著提高低照度图像的质量和可视化效果。此外，用户反馈也证明了该程序的可用性和易用性。

关键词：低照度图像增强；深度学习；卷积神经网络；客户关系；用户需求；用户反馈；用户体验

Abstract

Images captured under low-light conditions often suffer from noise, blur, and low contrast, which can affect the quality and visibility of the images. To address these issues, this paper proposes a deep learning-based low-light image enhancement program. The program is trained using convolutional neural networks (CNNs) to learn the features of low-light images and corresponding enhancement methods. By training on a large number of low-light images, the program can enhance new low-light images to improve their quality and visibility.

This paper also introduces the customer relationship of the program, including user requirements, user feedback, and user experience. The user requirements section includes the functional and performance requirements of the low-light image enhancement program. The user feedback section includes the evaluation and suggestions of the program for improvement and optimization. The user experience section includes the evaluation of the usability, reliability, and efficiency of the program.

Finally, this paper verifies the effectiveness and reliability of the program through experiments. The experimental results show that the program can significantly improve the quality and visibility of low-light images. In addition, user feedback also proves the usability and ease of use of the program.

Keywords: low-light image enhancement; deep learning; convolutional neural network; customer relationship; user requirements; user feedback; user experience