Low-light image enhancement is one of the most challenging tasks in computer vision, and it is actively researched and used to solve various problems. Most of the time, image processing achieves significant performance under normal lighting conditions. However, under low-light conditions, an image turns out to be noisy and dark, which makes subsequent computer vision tasks difficult. To make buried details more visible, and reduce blur and noise in a low-light captured image, a low-light image enhancement task is necessary. A lot of research has been applied to many different techniques. However, most of these approaches require much effort or expensive equipment to perform low-light image enhancement. For example, the image has to be captured in a raw camera file in order to be processed, and the addressing method does not perform well under extreme low-light conditions. In this paper, we propose a new convolutional network, Attention U-net (the integration of an attention gate and a U-net network), which is able to work on common file types (.PNG, .JPEG, .JPG, etc.) with primary support from deep learning to solve the problem of surveillance camera security in smart city inducements without requiring the raw image file from the camera, and it can perform under the most extreme low-light conditions.

中文翻译：

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使用注意U-Net的监控摄像机的超微弱图像增强。

弱光图像增强是计算机视觉中最具挑战性的任务之一，它已得到积极研究并用于解决各种问题。在大多数情况下，图像处理在正常照明条件下可实现出色的性能。但是，在弱光条件下，图像会变得嘈杂且黑暗，这使后续的计算机视觉任务变得困难。为了使隐藏的细节更清晰可见，并减少低光捕获图像中的模糊和噪点，需要进行低光图像增强任务。许多研究已应用于许多不同的技术。但是，大多数这些方法需要大量的精力或昂贵的设备来执行低光图像增强。例如，必须将图像捕获到原始相机文件中才能进行处理，并且该寻址方法在极端弱光条件下的效果不佳。在本文中，我们提出了一种新的卷积网络，即注意U-net（注意门和U-net网络的集成），它能够处理常见的文件类型（.PNG，.JPEG，.JPG等） 。）借助深度学习的主要支持来解决智慧城市中的监控摄像机安全问题，而无需从摄像机获取原始图像文件，并且它可以在最极端的弱光条件下运行。