Abstract
The usage of digital content (photos and videos) in a variety of applications has increased due to the popularity of multimedia devices. These uses include advertising campaigns, educational resources, and social networking platforms. There is an increasing need for high-quality graphic information as people become more visually focused. However, captured images frequently have poor visibility and a high amount of noise due to the limitations of image-capturing devices and lighting conditions. Improving the visual quality of images taken in low illumination is the aim of low-illumination image enhancement. This problem is addressed by traditional image enhancement techniques, which alter noise, brightness, and contrast. Deep learning-based methods, however, have dominated recently made advances in this area. These methods have effectively reduced noise while preserving important information, showing promising results in the improvement of low-illumination images. An extensive summary of image signal processing methods for enhancing low-illumination images is provided in this paper. Three categories are classified in the review for approaches: hybrid techniques, deep learning-based methods, and traditional approaches. Conventional techniques include denoising, automated white balancing, and noise reduction. Convolutional neural networks (CNNs) are used in deep learningbased techniques to recognize and extract characteristics from low-light images. To get better results, hybrid approaches combine deep learning-based methodologies with more conventional methods. The review also discusses the advantages and limitations of each approach and provides insights into future research directions in this field.
Abstract (translated)
数字内容（照片和视频）在多媒体设备普及的推动下，在各种应用程序中的使用量不断增加。这些应用包括广告活动、教育资源和社会网络平台。随着人们越来越注重视觉效果，对高质量图形信息的需求也在增加。然而，由于图像捕捉设备和光照条件的限制，捕获到的图片往往可见度低且噪声大。改善在弱光条件下拍摄的照片的视觉质量是低光照图像增强的目标。传统图像增强技术通过调整噪音、亮度和对比度来解决这个问题。不过，在这个领域最近的进步主要由基于深度学习的方法主导。这些方法有效地减少了噪声，同时保留了重要信息，并展示了改进低照明图像的巨大潜力。 本文提供了一种广泛的总结，概述用于提高低光照图像质量的图像信号处理方法。根据这种方法，三种分类被归入到回顾中：混合技术、基于深度学习的方法和传统方法。传统方法包括去噪、自动白平衡以及噪声减少等手段。在基于深度学习的技术中，则使用卷积神经网络（CNN）来识别并提取低光环境下图像的特征。为了获得更好的结果，混合方法将基于深度学习的方法与更传统的技术相结合。 回顾还讨论了每种方法的优势和局限性，并为未来的研究方向提供了见解。
URL
https://arxiv.org/abs/2502.05995
