Research On Dehazing And Enhancement Algorithm For Foggy Image

In recent years,Image defogging is a research hotspot of digital image processing.Fog is a molecule suspended in the atmosphere in the aerosol state.It has refractive and scattering effects on visible light,which can lead to significant degradation of imaging.The foggy image usually has some defects such as blurred details,low contrast and color distortion,which have a negative impact on image analysis,target recognition and other subsequent processing work.The visual quality of foggy images can be improved obviously by fog removal and enhancement,which is also an important part of subsequent image processing.There are two main image dehazing algorithms: image enhancement and dark channel prior image dehazing based on guided filtering.In view of the advantages and disadvantages of the existing two kinds of dehazing algorithms,this paper focuses on the synthesis of the two kinds of image dehazing methods to effectively improve the performance of image dehazing and enhancement algorithm.In this paper,the following two improved image defogging and enhancement algorithms are proposed.(1)fusion algorithm for foggy image enhancement based on transmittance weight factor is proposed.Firstly,the rough transmittance image of foggy image is obtained by the dark channel prior dehazing algorithm,and then the fine transmittance image is obtained by the guided filter.The dehazing image is obtained by the dark channel prior dehazing method based on the guided filter,and the enhanced image is obtained by histogram equalization based on the HSI color model.Finally,the weight factor is constructed by the fine transmittance,the above dehazing image and enhanced image are processed by weighted fusion.The algorithm gives full play to the respective advantages of two kinds of algorithms in foggy image processing,and significantly improves the fog removal and enhancement effect of foggy image.(2)To overcome the limitations of traditional image dehazing,an image enhancement algorithm for foggy image with sky based on sky segmentation is proposed.Firstly,based on K-means clustering and sky feature analysis,sky region recognition is performed for fog image with sky.Secondly,according to the pixels of sky region,the rough transmittance is corrected,and the dehazing image is obtained by dark channel prior image dehazing based on guided filter.Finally,the dehazing image is equalized by bi-histogram equalization.This algorithm effectively avoids the problem of color distortion and halo caused by the traditional dark channel prior dehazing algorithm in the sky area,and makes the restored foggy image have better global and local contrast.On the basis of theoretical analysis,this paper carries out simulation experiments with MATLAB,and optimizes the parameters of the algorithm reasonably through alarge number of sufficient simulation experiments.The simulation results show that the proposed algorithm can effectively restore the details of the image and enhance the global and local contrast of the image.It has a certain practical significance for improving the visual quality of the image and the results of subsequent image processing.