Research On Single Image Sharpness Restoration Algorithm Under Linear Constraint

Optical imaging systems are more sensitive to severe weather such as haze.Affected by the absorption and scattering of light by tiny particles such as water molecules in the atmosphere,the contrast,color saturation and visibility of images obtained under these conditions are greatly reduced,which seriously affects the use of images in computers and many vision systems.Image dehazing technology is designed to remove weather effects in hazy images and restore scene visibility and true colors.Therefore,the research on image sharpness restoration has important practical significance and practical application value in photography,video surveillance,ground observation and other computer vision applications.The dissertation takes the atmospheric scattering model in image restoration method as the base point.Aiming at some problems in the current dehazing algorithm,such as difficulty in handling haze in the depth field,difficulty in estimating the scene depth,and inaccurate transmission estimates,three feasible algorithms are proposed:(1)Gaussian adaptive standard deviation dehazing algorithm under linear constraint.Aiming at the problem that the transmission of dark channel prior algorithm is too small in bright areas such as the sky area and the minimum filter is insufficient,the algorithm first constructs a Gaussian function by using the minimum channel map of the hazy image to approximate the minimum channel effect of the haze-free image,thereby improving the accuracy of transmission in bright areas such as sky area.Then,in order to prevent the gray level of minimum channel of haze-free images from exceeding the range,a linear coefficient is proposed to constraint so that the gray level is distributed within a certain range.Secondly,it is observed that the standard deviation of Gaussian function is negatively correlated with haze concentration,so that an adaptive standard deviation is proposed to control the restoration effect,and transmission is optimized through cross-bilateral filtering to obtain the final restored image.Experiments show that the algorithm effectively restores contrast and detail information of images.(2)Fast single image dehazing algorithm combined with adaptive haze estimation.Aiming at problems in handling haze of depth field and estimating scene depth,the dissertation starts from the atmospheric scattering model and finds a correlation between scene depth and luminance components through experimental analysis,a linear coefficient is proposed and rough depth is approximately estimated,and the bright region is corrected by minimum filtering operation.Secondly,it is observed that the scattering coefficient value increased with haze concentration,so a concept of adaptive scattering coefficient is proposed by combining the haze concentration model and exponential function to estimate the accurate transmission.Experiment results show that the algorithm can recover a clear and natural haze-free image,significantly improves the restored image visibility,and is also effective for dense haze images.(3)Depth-constrained dehazing algorithm based on RGB channels.Aiming at problems in estimating scene depth and processing white areas such as sky area,the algorithm is inspired by the CAP prior,uses the positive correlation between arithmetic mean of light scattering intensity of RGB channels and haze concentration in images to estimate the scene depth.In rough scene depth,the gray value of depth area is large and the near is small,so a linear coefficient is used to constrain it.In addition,in order to eliminate the discontinuity between adjacent pixels,the algorithm uses L1 weighting function to optimize scene depth to make it closer to the real situation.Finally,the transmission and final clear image is obtained by atmospheric scattering model.Experiments show that the algorithm can process the image containing white objects and sky areas well.