Introduction to real-time video fog monitoring technology

The currently known fog monitoring algorithms can be roughly divided into two categories: one is a non model image enhancement method, which enhances the contrast of the image to meet the subjective visual requirements and achieve clarity; Another method is model-based image restoration, which examines the causes of image degradation, models the degradation process, and uses reverse processing to solve the problem of image restoration.

Security products will be applied in various complex scenarios and harsh weather conditions. Real time monitoring around the clock places strict requirements on the portability, power consumption, processing effectiveness, and adaptability of the products. A good video fog transmission technology should integrate the technical advantages of image enhancement and image restoration based on the atmospheric transmission model, so as to achieve ideal image effects and be applied in practical engineering.

After fully analyzing the advantages and disadvantages of the theory of fog penetration and conducting in-depth research and exploration, Shenzhen Anxing has developed a real-time video fog penetration technology based on the special requirements of video image fog penetration in the field of security monitoring.

This technology is based on the principle of atmospheric optics, which distinguishes the depth of field and fog concentration in different areas of the image for filtering processing, and obtains accurate and natural transparent fog images.

This real-time video fog transmission technology can automatically adjust according to changes in fog conditions to adapt to various scene applications, avoiding the effect of excessive blackening of close range fog transmission and blurred distant view; At the same time, it balances the efficiency and complexity of implementation, ensuring the real-time and engineering feasibility of the entire fog transmission.

At the same time, this real-time video fog transmission technology can not only effectively remove the impact of fog, but also avoid color errors caused by transitional processing of certain scenes, as well as the unreal feeling caused by excessive fog removal.