Concerning low efficiency detection for foggy density in traffic image

a novel algorithm was proposed to check foggy density based on distribution characteristics of natural statistics.Firstly

foggy images were partitioned as PP pixel patches by method of the maximum overlap count.Secondly

featured function vector for local contrast and entropy was created and maximum likelihood estimation between tested image and two standard image corpuses were respectively computed.Finally

Mahalanobis-like Distances(MD) between foggy image and corpus of standard foggy image or fog-free image were achieved

and the ratio of two values could be used to measure the foggy density.Simulation shows that the value D can respond the varied tendency to density for same scene with difference density or different scene with different density.Correlation coefficient up to 0.97 between this algorithm and mean opinion scores (MOS) method indicate high linear about them and the coefficient is larger than 0.56 between mean subtracted contrast normalized (MSCN) and MOS.Comparison to PM2.5 shows that this algorithm can be used to evaluate the level for fog density.