Nowadays, with the development of the economy and the improvement of people's living standards, the scope of video surveillance in life is becoming wider and wider, and people are particularly concerned about the application and development of video processing technology in the new situation.
Digital video and digital images are higher resolution than traditional image and video, easy to handle, easy to operate and organize. However, due to factors such as insufficient performance of some devices and limitations of objective conditions, in actual video surveillance applications, problems such as blurred video images and incapable key information capture may still occur. In the process of video image processing, due to operational technical problems or objective factors, it brings some negative effects to the application of video image processing technology, and reduces the level and quality of processing technology.
Four major technologies of video image processing technology
The process of video image processing involves the process of collecting, transmitting, processing, displaying and playing back video image data. These processes together form an overall cycle of the system and can operate continuously. The most important in the scope of video image processing technology is the compression technology of images and the processing technology of video images. At present, the mainstream video image processing technologies on the market include: intelligent analysis processing, video through-fog anti-reflection technology, wide dynamic processing, and super-resolution processing. The above four processing technologies are respectively introduced below.
Intelligent analytical processing technology
Intelligent video analysis technology is an important means to solve the problem of big data screening and retrieval technology in the field of video surveillance. At present, domestic intelligent analysis techniques can be divided into two categories: one is to detect the movement of objects in the picture by means of foreground extraction, and to distinguish different behaviors by setting rules, such as mixing lines, item legacy, perimeter, etc. The other is to use pattern recognition technology to specifically target the objects to be monitored in the picture, so as to achieve detection and related applications of specific objects in the video, such as vehicle detection, human flow statistics, face detection and other applications. .
Video through fogging technology
Video fog-enhanced technology generally refers to clearing images that are unclear due to fog and moisture, emphasizing certain features of interest in the image, suppressing features that are not of interest, and improving image quality, information More abundant. Due to the smog weather and the harsh conditions such as rain, snow, strong light and dark light, the image contrast of the video surveillance image is poor, the resolution is low, the image is blurred, and the features are not recognized. The image after the anti-reflection process can be the next step of the image. The application provides good conditions.
Digital image width dynamic algorithm
Wide dynamic range in digital image processing is a basic feature that occupies an important position in image and visual restoration, and is related to the image quality of the final image. The dynamic range is mainly determined by the protection semaphore and the average noise ratio, where the dynamic range can be defined from the perspective of light energy.
Digital signal processing is affected by the exposure, illumination, and intensity of the exposure. The dynamic range is closely related to the depth of the pattern. If the dynamic range of the image is wide, the brightness changes more obviously during image processing, but if the dynamic range is narrow, the change in brightness and darkness is not obvious when the brightness is converted. At present, the wide dynamic range of images is widely used in video surveillance, medical imaging and other fields.
Super resolution reconstruction
The most straightforward way to increase image resolution is to increase the sensor density of the acquisition device. However, high-density image sensors are relatively expensive and unaffordable in general applications; on the other hand, imaging systems are currently approaching the limit due to the density of their sensor arrays.
An effective way to solve this problem is to use a signal processing-based software method to improve the spatial resolution of the image, that is, super-resolution (SR: Super-ResoluTIon) image reconstruction. The core idea is to use the time bandwidth (to obtain the same scene). The multi-frame image sequence is exchanged for spatial resolution to convert the temporal resolution to the spatial resolution, so that the reconstructed image has a visual effect that exceeds any one frame of the low-resolution image.
in conclusion:
In the process of video surveillance application, as people's requirements for monitoring image quality are getting higher and higher, the practical value of improving surveillance images has become a new requirement put forward by society to the whole security industry. In this form, the current mainstream video image processing technology must keep pace with the times to meet the changing needs of users.
Ipa Snap Swab For Atm Cleaning,Snap Swab For Atm Cleaning,Swab For Atm Cleaning,Ipa Swab For Atm Cleaning
Miraclean Technology Co., Ltd. , https://www.mrccleanroom.com