Tech. Parameter:
• Title OSD: Supports channel name, date, and video stream information overlay;
• Privacy Mask: 4 configurable regions
• Image Orientation: image flip and mirror
• Auto Exposure Control (AEC); Auto White Balance (AWB); Auto Band Filter (ABF)
• Auto Black Level Calibration (ABLC)
• Network protocol: TCP/IP UDP RTP RTSP RTCP HTTP DNS DDNS DHCP FTP NTP PPPOE SMTP UPNP
• ONVIF protocols: Support 2.0 Version
• Remote View: IE 6.0 or later, Firefox, chrome
• Network Interface: 10/100M BASE-TX,RJ-45 connector
General Specifications:
• Operating Temperature: -20 °C ~ 50 °C (-4 °F ~ 122 °F)
• Operating Humidity: 0% -85% RH
• Power: 48V PoE or 12V DC
Network Camera Another name including:
Wisenet Camera, Poe Security Camera ,Wisenet Security Camera, Wireless Ip Camera , Bullet Network Camera , IR Network Camera ,Dome Network Camera,Starlight IP Camera, PTZ Network Camera
Network Camera Poe Security Camera,Wisenet Security Camera,Wireless Ip Camera,network camera system Shenzhen Sanan Technology CO.,LTD , https://www.sanan-cctv.com
Introduction <br> <br> lower in the process of digital image acquisition, due to the impact equipment performance and shooting conditions, to make the acquisition of image resolution. Such images are relatively vague and have a greater impact on later processing and applications. Therefore, improving the resolution of images is something we must solve. The most straightforward way to increase resolution is of course to use higher-resolution devices, but there are two problems: First, high-resolution devices are expensive; second, each device has its limits and is subject to hardware devices. The limitations are hard to get really high resolution images. Therefore, we can consider the use of software methods to improve the resolution of the image. This is the SR (Super-Resolution) image reconstruction discussed in this paper.
Super Resolution Implementation Techniques There are many ways to implement super resolution resolution techniques. Here we describe a few of the most commonly used methods:
1) Based on interpolation. This method is the most intuitive method in current superresolution research. By comparing and estimating multiple frames of the image and obtaining the relative relationship information between them, the pixel values ​​of the high-resolution image at non-uniform pitch sampling points are obtained. Then through a non-uniform interpolation method, after a certain interpolation, a high-resolution image can be obtained. Of course, the image thus obtained may have problems such as noise, blurring, and the like, so it can be repaired by using image restoration technology.
2) Based on reconstruction. This method mainly has two key steps of registration and reconstruction. In registration, multi-frame low-resolution images are used as constraints for data consistency, so that sub-pixel precision relative motion between other low-resolution images and reference low-resolution images can be obtained. During reconstruction, the target image can be optimized using prior knowledge of the image. The common algorithms of this method include iterative direction projection, maximum posterior probability, convex projection and so on.
3) Based on learning. The premise of this method is that the low-resolution image is completely owned with information for reasoning and predicting the corresponding high-resolution part. In this way, a low-resolution image set can be trained to generate a learning model, which can calculate high-frequency details of the image. At present, commonly used learning algorithms are the Example-based method proposed by Freeman et al., the neighborhood-based embedding method proposed by Chang et al., and the like. 
Super-resolution image reconstruction has a very wide range of uses in real life. Here, we have listed some of the places used in our lives:
1) Digital HD. In the field of digital television, super-resolution reconstruction technology can be used to convert digital television (DTV) signals into matched signals for high-definition television (HDTV) receivers, thereby enhancing the viewer's experience with video surveillance.
2) Medical images. In medical care, high-resolution medical images are very helpful for doctors to make a correct diagnosis. Therefore, using super-resolution reconstruction to obtain a clearer image will enable doctors to treat more accurate, effective, and magnetic resonance imaging.
3) Satellite image analysis. In military and meteorological fields, similar objects can be easily distinguished from similar objects using high-resolution satellite images. Therefore, super-resolution reconstruction technology can be used to obtain high-resolution images, better serve military security and daily life, satellite imaging: remote sensing, telemetry, military reconnaissance and so on.
4) Safety testing. Banks, residential areas, and road junctions are places where security testing is required. Although cameras are generally installed in these places, the images are very blurry. The use of super-resolution reconstruction technology will help the staff to get a clearer image, that is, to help with normal safety management, and to help the case when the case occurs.
5) video format conversion, video enhancement and restoration: the remake of old movies, etc.;
6) microscopic imaging, virtual reality <br> <br> achieve super-resolution effect
In many cases, people want higher image resolution and clearer images. However, the limitations of the actual hardware conditions, as well as other factors, we get the resolution of the image and can not meet the requirements. We can use software-based methods to use super-resolution image reconstruction techniques to optimize and repair images. Through the following pictures, we can see the effect of super-resolution.
1. In Figure 1, the use of super-resolution technology to obtain blurred text in vase patterns. 
2. Figure 2 shows the application of the super-resolution method in medical images. The figure shows a super-resolution reconstruction technique that uses multiple low-resolution image sequences (carotid MRI images) to obtain relatively clear images. 
Figure 2 shows a clear picture of the carotid artery through a set of low-resolution image sequences (carotid MRI images). Figure 3 shows the application of super-resolution techniques in streaming video enhancements, limited by the limited video bandwidth. Improve the clarity of the video. 
Future development of super-resolution
Due to its wide range of applications, people have conducted extensive research on super resolution image reconstruction techniques in the last 20 years, and this technology has also been rapidly developed. We have already used this technology in satellite meteorology, medical imaging, and image compression. However, there are still many problems that need to be solved in this area, and degraded models, motion estimation, reconstruction algorithms, and real-time applications will be the focus of future research. However, we have reason to believe that in the future there will be more and more areas that benefit from the further development of super-resolution technology. It can be said that the development of super-resolution image reconstruction technology has been and is further influencing and improving our lives. All aspects.
Super-resolution Overview Super-Resolution is to increase the resolution of the original image through hardware or software. A series of low-resolution images is used to obtain a high-resolution image. The process is super-resolution. reconstruction. The core idea of ​​super-resolution reconstruction is to use the time bandwidth (obtaining multiple frames of image sequences of the same scene) in exchange for spatial resolution, and realize the conversion of temporal resolution to spatial resolution. Super-resolution application scenario
Figure 1 from the vase pattern to get vague text
Figure 3 Using Super Resolution Technology to Improve Video Definition