With the rapid development of computer image processing technology, the increasing popularity of information technology, and the improvement of people's security awareness, real-time video surveillance systems have gradually become an indispensable part of people's lives, and have been widely used in production, transportation, and society. Security and other aspects. However, due to the limited weather conditions (fog, rain, wind, light, etc.) and the monitoring system's own technical conditions, video images often do not achieve the desired results. The picture quality is degraded or ambiguous, which leads to difficulties in operations such as identification, forensics, and event analysis, which makes the system unable to be applied normally. Therefore, the research and application of fuzzy image processing technology is of great significance in the field of security.
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Analysis of the Causes of Fuzzy Image Generation
System factor
In a full analog monitoring system, from front to back, it consists of image acquisition, image transmission, image storage, and image display. At each link, video information loss may occur, that is, the image quality may be deteriorated or blurred.
Lens: affecting the luminous flux and imaging accuracy of entering the camera, it will directly lead to image blur; Camera sensor: affects the acquisition and photoelectric conversion of the optical signal, which will directly lead to image blur; BNC connector at both ends of the video transmission cable: The signal shielding gap will cause signal loss. The video transmission cable will transmit over a long distance. The transmission cable's resistance, shielding, impedance matching and other issues will cause signal attenuation, which will directly lead to image quality deterioration and blur. There will also be some signal loss at the image rendering end.
In an all-digital video surveillance system, network transmission, digitally encoded video signal transmission and storage relative to the analog system, can more effectively avoid image damage caused by signal attenuation. However, image signal loss during lens, image acquisition, and back-end rendering is still unavoidable. In addition, in the digital video surveillance system, the A/D conversion and video coding compression of the video signal are added, and these links still cause loss of image information. Existing video compression coding algorithms are lossy compression, which directly leads to the loss of video information and affects video clarity.
Natural environment
In addition to the system itself, the natural environment has a great impact on video image clarity. If you encounter natural weather such as wind, rain, snow, fog, etc., the image quality will be drastically reduced or blurred. In addition, there are insufficient illumination, backlight, backlight, low temperature or too high, which will affect the image restoration system and affect the image clarity. In the case of insufficient light, the camera's Sensor imaging produces a lot of noise, which affects the sharpness of the image and greatly increases the code stream of the image.
Artificial environment
The power supply of the power supply system is not “cleanâ€, that is, it breaks into a relatively strong interference signal, specifically refers to superimposing an interference signal on a 50 Hz sine wave. If there is a high-power thyristor frequency modulation speed regulation device in the power grid, the thyristor rectifier Devices, thyristor AC/DC converters, etc. can cause pollution to the power supply.
There is a strong source of electromagnetic interference or electromagnetic radiation near the TV monitoring system. Electromagnetic interference sources such as electric welding, radio transmission, large motors, and large relays can also cause interference with video signals. Electromagnetic interference can result in images with equally spaced vertical bars or images with regular flickering stripes, etc., resulting in blurred images. Also, it is caused by vandalism, such as a license plate that is made difficult to recognize, so that the camera cannot take the license plate number and the like.
Development of Fuzzy Image Processing Technology
Since the 1990s, the security industry has experienced a period of rapid development. With the development of security monitoring systems from full analog to full digital, people have higher and higher image quality requirements, and image resolution has also experienced changes from CIF to standard definition to high definition.
Early traditional monitoring systems used analog signal transmission, called the first generation of full analog surveillance era, when video files were recorded and saved by video tape recorders. Because the storage of video images is based on analog devices, the analog storage devices will have a certain amount of information loss as the usage time increases. However, the fuzzy image processing technology is based on digital image processing technology, so in the pure simulation era, it has not been introduced into the security field.
Due to the inherent defects of analog monitoring systems, digital storage has gradually replaced analog tape storage with the continuous development of computer technology. Then it entered the second-generation digital surveillance system, which is based on the digital hard disk recording DVR, replacing the analog video recorder and taking the first step of digital surveillance. After the video images were digitized, image processing technology began to come into play, and fuzzy image processing technology was gradually introduced into the security field.
With the development of computer and network technology, video surveillance has now developed into an era of full digital surveillance based on IP networks, thus entering the third generation of all-digital network video surveillance era. The representative products of this era are mainly IPC and NVR. After the video image is digitized, the fuzzy image processing technology has been widely used in the security field.
The fuzzy image processing technology belongs to a kind of image processing technology, which uses a computer to calculate or process digital signals in image information in order to improve the quality of the image and achieve the desired result, so it is also called computer image processing technology. For example, noise is removed from noise-contaminated images, images with weak information are enhanced, and geometric images are corrected for distortion. In recent years, with the increasing awareness of people's safety and the increasing demand for security in society, surveillance image processing technology will continue to evolve in such market demand and law, and play an increasingly obvious role.
Special requirements for the security industry
Computer image processing converts an image signal into a digital signal and processes it with a computer. Because the processing speed of the computer is extremely fast, and the digital signal has the characteristics of small distortion, easy storage, easy transmission, strong anti-interference ability, computer image processing is widely used, including aerospace, telemetry, medical equipment, industrial automation detection. , security identification and other major areas. Each application area has its own special requirements, and its application in the security monitoring industry also has its inherent particularity:
1. High requirements for image clarity. At the public security monitoring site, the public security organs often need to monitor the video to identify suspects, evidence, etc., and generally low-definition video does not meet this requirement. At the traffic monitoring site, traffic police need to monitor the images to identify license plates, violations, drivers, etc., and blurred images cannot be applied in this case.
2, the monitoring of different industries, the image requirements are different. For example, medical monitoring requires a higher color reproduction of images. Intelligent traffic monitoring requires relatively high night illumination and capture speed of the camera, and requires clear identification of the license plate. In unattended monitoring, equipment needs to be stable for long periods of time under unsupervised conditions.
3, outdoor installation, unattended. In the field of security, most of the equipment needs to be installed outdoors, and the equipment needs to withstand the wind and sun for many years. The aging of electronic devices themselves will be relatively faster than in other fields. Aging of cameras, lenses, transmission lines, etc. can cause images to become increasingly blurred.
4, the requirements of the number of massive video channels. In the large-scale safe city monitoring project, the number of video channels will reach tens of thousands of roads and even more. Therefore, in the video surveillance field, it is expected that the code rate compression ratio of the video coding is the highest, thereby reducing the bandwidth and storage capacity requirements. This results in more information loss in the video encoding process, resulting in blurred images.
These special application requirements in the security field will lead to a decline in image sharpness, which in turn has high requirements for image sharpness, which will inevitably lead to a broad application prospect in fuzzy image processing technology.
Limitations of Fuzzy Image Processing Technology
At present, due to factors such as hardware technology level, transmission bandwidth and application environment, the image blurring problem cannot be completely solved.
From image acquisition, transmission, storage to display, any aspect is critical to image quality. Any problem with a single step can affect image quality, and this effect is irreversible. Therefore, to completely solve the image blur problem requires a comprehensive technical update. For example, in the context of current digital image technology, coding technology is one of the bottlenecks affecting image quality. If there is an encoding algorithm with high compression ratio and small image loss, of course, the image blurring problem caused by compression will be solved to some extent. However, to achieve this algorithm effect, usually requires a higher computational cost, so an update of the hardware technology is needed to satisfy such an algorithm.
Like the super-resolution reconstruction of fuzzy image processing technology, due to the complexity of the algorithm, the current conventional equipment is still difficult to process high-definition images in real time, so the efficiency of the algorithm is still one of the reasons for the current lack of fuzzy image processing, which is It is necessary to start from both the algorithm and the hardware to improve the efficiency of the algorithm and also to improve the hardware performance.
In addition, various algorithms for fuzzy image processing are currently based on a specific scenario application. The locality and limitations of various algorithms have caused obstacles in algorithm application. So in the years to come, there are still a long way to go about algorithms and models for image processing.
Development trend and outlook
Broadly speaking, images that do not have enough information can be called blurred images, so the image blurring problem will have new performance as people's needs increase. For example, the current monitoring field may only need a certain resolution image to meet the needs of face recognition. With the development of society, we may need to analyze someone's mouth movement through a video to analyze his What to say, in this case would require a higher resolution and clearer image. This will be an eternal pursuit of direction, so the problem of fuzzy image processing will be studied all the time.
As part of the Internet of Things, video surveillance will eventually develop in the direction of intelligence with the continuous development and application of the Internet of Things. Automatic analysis of image blur becomes one of the technologies for system self-test application. Through intelligent analysis, the system will Image geometry, color, noise, blur, blend, and super-resolution image effects are automatically recognized for automatic analysis and processing. With the wider application range of fuzzy image processing technology, the perfect combination of fuzzy image processing technology and intelligent analysis will inevitably become a development trend.
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