Research On Image Dehazing Processing Model And Algorithm

Under hazy weather,due to the influence of turbid media(e.g.,suspended particles,water droplets)distributed in the atmosphere,images captured by camera always feature poor visibility and dim color.These hazy images can lead to significant performance deterioration of computer vision-based systems,such as target recognition,autopilot and behavior detection.Although the infrared camera has stronger penetrating power and it can alleviate the blurring problem of the captured image to some extent,its market share is still low due to the high-power consumption,high price and cumbersome maintenance.Therefore,research on a restoration technique for hazy images is of extreme importance.According to the in-depth analysis of the limitations of existing atmospheric scattering model(ASM),this paper firstly concise and propose several degradation imaging models with enhanced robustness,including: pixel-based atmospheric scattering model(Pb-ASM),scene-based atmospheric scattering model(Sb-ASM)and the gamma-form atmospheric scattering model(Gf-ASM).Subsequently,several improvement strategies are developed in order to overcome the problems faced by dark channel dehazing technology.Finally,based on the traditional ASM,Pb-ASM,Sb-ASM and Gf-ASM,the corresponding image dehazing algorithms or image enhancement method are also further proposed.In summary,the core contributions of this paper are mainly focused on the following aspects:(1)To improve the efficiency of the dark channel dehazing method,an edge replacement method(ERM)is developed to replace the soft-matting operator employed in original algorithm.The proposed ERM is dominated by the gradient priority law,and the block effect problem at the depth-discontinuity regions is repaired by using the high correlation feature of local information.This paper also provided a based-double-threshold transmission refine mechanism to deal with the invalid case of dark channel prior.Aiming at the problem of over-darkness of the restored scene,a nonlinear contrast stretching algorithm is proposed to highlight the necessary texture details under the premise of ensuring the integrity of the brightness information(2)Using the existing ASM as the theoretical basis for image degradation,this paper proposed a single image dehazing method based on haze concentration estimation.This algorithm mainly focused on three core issues of atmospheric light estimation,transmission map estimation,and image enhancement.First,the haze concentration prediction mechanism is established via the intrinsic link between haze density and texture blur,from which it identifies the thickest area by fuzzy clustering algorithm and then precisely estimates global atmospheric light.Considering the visual impact of contrast and saturation on the image,an optimal evaluation index is designed for detecting the transmission map.Combined with this proposed index and scenes correlation,the transmission estimation process can be simplified to one-dimensional search problem.At last,a multi-scale sharpening algorithm based on wavelet domain is proposed to remedy the defect of dim scenes.(3)On the basis of redefined Sb-ASM,this paper adopted the recovery idea of “Positive estimation?Reverse reduction” and gradually estimated the unknown parameters involved in Sb-ASM,thereby realized the haze removal from single hazy image.In the proposed method,the different scenes are firstly identified by using the clustering strategy,and the ambient lights in all scenes are estimated via the modified corrosion method;Secondly,an adaptive adjustment contrast prior based on environmental features is applied to estimate the each scene transmission;Finally,since the edge of the segmentation graph cannot be completely consistent with the depth of the original real scene,a guided variation model is provided to address this shortcoming.(4)Based on the Sb-ASM,a fast single image dehazing algorithm is also presented.In this algorithm,by constructing a linear model between the transmission and the haze thickness feature,the transmission map can be directly estimated through a linear operation on three components: luminance,saturation and gradient.Then,with the estimated transmission map and the proposed guided energy model(GEM),we can easily estimate the global atmospheric light(GAL)and scene incident light(SIL),and restore the scene albedo via the IASM.In addition,an accelerating framework based on Gaussian-Laplacian pyramid is proposed to increase the computational speed.(5)On the basis of redefined Pb-ASM,a single image dehazing method based on Bayesian theory is proposed by using the "inverse estimation ? positive reduction" idea.The proposed method firstly combined the Pb-ASM with the Bayesian model to evolve the dehazing problem into the maximum posterior probability model.Subsequently,the probability density involved in the posterior model is constructed by using the existing priori knowledge.Due to the global recovery strategy employed for merging the advantages of the image priors,this method can produce a promising haze-free result.(6)Taking the proposed Gf-ASM as the imaging basis,a universal image enhancement method is proposed in this paper.Through a large number of experimental statistical tests,this paper explored and proposed a bright channel prior summarized from thousands of high-definition images.Using this a prior and dark channel prior,Gf-ASM can be constrained and thus derived into a scene albedo recovery formula.Benefitting from the robustness of Gf-ASM,the derived scene formula also can be used for other different types of degraded images.