Fog may cause the impairment of image as well as the decrease in the distinguish ability. The present paper is to get rid of the weather’s influence from the impaired image. According to Retinex theory and exponential relationship between the degradation of the image and the depths of the scene points, it puts forward a fog-removing treatment based on combining high-frequency emphasis filtering and histogram equalization .Firstly, obtain the padding parameters and fill it. Secondly, filter the impaired image using Butterworth highpass filter of order 2. Through the padding parameters, Highpass filtering is not overly sensitive to the value of cutoff frequencies, as long as the radius of the filter is not so small that frequencies near the origin of the transform are passed. In which the gray-level tonality due to the low-frequency components was retained. Lastly, histogram balanced the image gotten last step. The simulation result based on Matlab shows his algorithm can effectively improve the visual effect scene under the condition of mist.
Due to these reasons that present frequent neighboring class set mining algorithms are unsuitable for extracting any length frequent neighboring class set with constraint class set, this paper proposes an algorithm of synchronous mining frequent neighboring class set with constraint class set, which is suitable for mining any length frequent neighboring class set with constraint class set in large spatial database. The algorithm creates mining database through digitization method, and then gains candidate frequent neighboring class set with constraint class set via synchronous search strategy, namely, it uses computing (k-1)-subset of k-non frequent neighboring class set with constraint class set to generate candidate frequent neighboring class set, meanwhile, it also uses connecting (l+1)-superset of l-frequent neighboring class sets to generate candidate frequent neighboring class set with constraint class set, it only need scan database once to extract frequent neighboring class set with constraint class set. The result of experiment indicates that the algorithm is faster and more efficient than present algorithms when mining any length frequent neighboring class set with constraint class set.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.