Adaptive Judgment Method of Fog Concentration

Abstract

The invention discloses a fog concentration self-adaptive judging method. The method includes a construction stage and a judgment stage. In the construction stage, a dictionary is constructed first, and the constructed dictionary is used as a basis for image query to obtain image quality evaluation scores , further, determine the fog concentration judgment threshold by querying the exact fog concentration image; in the judgment stage, continuously acquire the current frame image and perform image query on its image blocks to obtain the quality evaluation scores of all image blocks in the frame, and then according to all The average evaluation score of the block and the fog density judgment threshold determine the fog density status of the current image. In the present invention, a threshold judgment process is added to obtain an adaptive mist concentration judgment method with better consistency between judgment results and human subjective judgment results and more stable judgment results.

Description

technical field [0001] The invention relates to digital image processing, in particular to a fog concentration adaptive method. Background technique [0002] There are few current adaptive judgment methods for fog concentration, and the judgment results of existing algorithms are quite different from the results of human subjective judgment. The judgment results are easily affected by noise, which makes the effect of adaptive fog concentration judgment algorithm difficult to meet the needs of automatic defogging. Affects video surveillance image quality. [0003] The existing technology usually extracts a certain feature information from the image, gives the threshold value for distinguishing different fog concentration states through experience, and finally uses the feature information to directly judge the fog concentration type. The judgment result is unstable due to the influence of noise, etc. At the same time, the consistency with human subjective judgment is not good...

AI Technical Summary

Problems solved by technology

However, there are some problems in this algorithm that seriously affect the image effect: first, only use filters with edge-guided filtering characteristics such as guided filtering / bilateral filtering, and there are still obvious halos at the strong edges of the dark channel image; second , The dark channel principle believes that there are a large number of highly saturated colored objects, shadows and dark areas on the fog-free image. However, this assumption does not hold true for white sky and light-colored buildings. Direct application of this principle will lead to excessive defogging of this part It is not natural enough, and even noise appears; third, the ambient light estimation has a certain influence on the effect of the image restoration by the defogging algorithm. At present, there are few studies on ambient light estimation, and the mainstream algorithm uses the calculation of ambient light with the highest brightness.

[0016] The defogging algorithm based on the physical degradation model, if the estimation of the ambient light is inaccurate, the image will be brighter or darker after defogging

Images