Multi-exposure Image Registrstion And Fusion Based On Image Low-level Fature Consistency

In the current digital image imaging,there is a mismatch between the imaging device and the dynamic range in the scene.It is difficult to complete the complete extraction of the information in the scene with one exposure.Multi-Exposure Image Fusion(MEF)provides a concise method of generating HDR images.Two major technical problems facing the existing MEF: removing ghosting in dynamic scenes,and processing detailed information in static scenes.To sovel this two technical problems,this study proposes a novel MEF framework,which named the multi-exposure fusion method based on the consistency of the low-level features of the image.This framework starts with consistent analysis of image patches structure and inconsistent pixel correction,and using image brightness and moderate exposure,contrast,structure vector and other low-level features to achieve image fusion.Then combined with gamma correction and saturation adjustment,the multi-exposure fusion technology is applied to the single image dehazing.details as follows:1.In order to resolve the problems of information preservation of MEF in the static scene,we propose a novel MEF method based on the consistency of image low-level features.Through guided filter to decompose the image to obtain the base layer and detail layer.The image patch structure decomposition algorithm and appropriate exposure evaluation are used to achieve the fusion of the image base layer and the detail layer respectively.Then the fusion image is obtained.2.In order to resolve the problem of inconsistent object motion in dynamic scenes,a dynamic scene multi-exposure fusion method is proposed,which based on the consistency of image low-level features.Calculate the directionality of the structure vector through image patch decomposition to detect the structural consistency of the image patch.Thereby identifying the area in the image that is inconsistent with the reference image motion.Then use the brightness mapping function to correct the inconsistent areas.Finally applying the multi-exposure fusion method in static to obtain the fusion result without ghosting.3.For the application of multi-exposure images in practice,the multi-exposure image fusion technology is applied to foggy image processing.A series of gamma corrections and enhance the color saturation are applied to obtain a set of underexposed image sequences.Then a multi-exposure fusion method based on adaptive decomposition of image patch structure is applied to collect from each image the areas with the best contrast,saturation,and texture quality to fuse into a single fogless image.The experimental results show that the multi-exposure fusion method based on the consistency of low-level features can effectively remove ghost images in dynamic scenes.For static scenes image sequences,this method provides good exposure,color saturation,sharpness,and local details in the image.It has a good performance,the average value of the quality perception index of the fusion result is 0.9746.In the single image dehazing,the combination of gamma correction and multi-exposure fusion method provides a dehazing result that is more in line with human visual observation,and the average the haze concentration was 0.358,and the average structural similarity error was 0.7816.