Study And Application Of Image Quality Assessment Of Enhanced Images

Image enhancement can improve the visual effect of the image,so that people or machines have a better experience and use value.The enhancement of image quality has a direct impact on human visual perception and access to information accuracy,but enhanced image quality evaluation of the relevant research has not been widely concerned.In order to meet the demand of image application,it is very important to design a stable and reliable image quality evaluation method.In this paper,the image quality evaluation is studied in two aspects,whic h are the features of enhanced image and the characteristics of human visual perception system.Therefore,two effective methods of NR quality evaluation and haze enhancement based on image quality are proposed.Main works of this paper are as follows.1.Enhanced image NR quality evaluation is proposed.There is no unified,publicly-enhanced image database.Firstly,an Enhanced Images Database(EID)with 320 images is constructed,and subjective scores are obtained by subjective experiments.Then,nonstructural information,sharpness and Natural Scene Statistic information are computed respectively,producing 42 perceptual features.Support Vector Regression(SVR)is employed to train the quality model.Finally,the trained model is used to predict the quality of the image to be measured.The experimental results in EID?CID2013?LIVE WILD and DRIQ show that the proposed algorithm can maintain a high consistency with the subjective evaluation results,and its performance is superior to the general NR image quality evaluation algorithms.2.Dehazed image quality evaluation method based on residual haze measurement is proposed.Firstly,an improved Dark C hannel Prior(DCP)method is proposed to measure the density of residual haze.At the same time,the contrast characteristic factors affecting image quality are put forward.Finally,according to the characteristics of Human Vision System(HVS),the color saturation of haze image is analyzed,and the saturation of image is found to be the main factor affecting the distortion of the image.Finally,three sets of features are trained on SVR.Then,the trained SVR model is used to estimate the image quality.Experimental results demonstrate that the proposed algorithm can assess the image quality accurately and produce quality scores highly consistent with human vision in the public databases.3.Image dehazing method based on image gradient distribution is proposed.Firstly,a prior model of image gradient distribution is obtained from the training of high quality natural image data.Secondly,the gradient distribution of haze image is modified so that it approximates theprior model.Finally,the reconstructed image is obtained by using Poisson equation.Experimental results reveal that the proposed algorithm can be used for image dehazing effectively.Compared with the current dehazing algorithms,the proposed method retains more detail information and improves the image quality significantly.