In low light environments, the collected images have characteristics such as low contrast, low signal-to-noise ratio, and loss of details, leading to an overall decline in image quality. Low light image enhancement aims to restore images with complete details and has gradually become a research hotspot in computer image processing. With the large-scale increase in data volume in recent years, deep learning based methods have gradually become mainstream. This article provides a detailed classification and analysis of low light image enhancement methods based on deep learning, sorts out various networks used, introduces the basic principles and steps of various algorithms, and introduces the existing low light image enhancement datasets and evaluation methods. Finally, a summary of the content was made, pointing out the difficulties in current research and providing prospects for future research directions.
In order to solve the problem of fire detection in the heat exchange station, an intelligent fire early warning system based on image analysis is proposed, which realizes the network, digital and intelligent design of video monitoring system. This paper introduces the framework of the platform and the functional structure of each subsystem, gives the overall structure diagram of the system, focuses on the basic algorithm of background modeling, flame segmentation and morphological filtering in the fire identification and analysis module, and puts forward the design scheme and workflow of the alarm and linkage module. Experiments show that the system has low complexity and good real-time performance, which can improve the efficiency of fire emergency command in heat exchange station and meet the actual requirements of heating security.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.