Research On Underwater Image Degradation Processing And Image Enhancement Algorithm

The ocean is a treasure house rich in oil and gas resources,biological resources and mineral resources.Ocean exploration requires efficient underwater detection methods.Underwater vision technology provides important support for underwater exploration.The quality of underwater videos and images directly affects the effectiveness of underwater operations.However,the complex and changeable underwater environment poses challenges for obtaining high-quality visual images,including:(1)The selective absorption of seawater to different wavelengths of light results in color reduction of underwater images.(2)The scattering of suspended particles results in the blurring of the target scenery.(3)Underwater biological motion,turbulence and other factors cause the blurring of underwater images.These degradations restrict the performance and effects of underwater vision applications such as target detection and recognition,key-point matching,and image segmentation.Therefore,improving underwater image sharpness has become an urgent need for underwater vision tasks.Firstly,to solve the problem of the small number of samples and limited types of underwater images,we establish four underwater image simulation models:The light absorption model and light scattering model are established by simplifying the classic Jaffe-McGlamery underwater image imaging model.Besides,we build a turbulent fuzzy model by improving the power spectrum of Kolmogorov.According to the disc defocusing function,we established the defocusing model.By adjusting the parameters of these simulation models,you can quickly simulate a variety of pictures close to the real situation underwater.Secondly,we propose a multi-scale fusion framework that combines image restoration and image enhancement.The defog-enhanced image and the white balance image are used as the input image and the four weights of the input image are calculated.After that,the input image and the corresponding weights are decomposed by Gaussian pyramid and Laplacian pyramid,and the final clear image is obtained by multi-scale fusion.The defogging algorithm estimates the RGB channel transmission map according to the color clustering algorithm,and reconstructs the red channel transmission map with the most severe underwater attenuation according to the color attenuation curve.Experiments show that the processing results of the algorithm can effectively remove fog and turbidity,eliminate color cast,and improve image contrast.Finally,we also proposes a comprehensive framework combining traditional algorithms and deep learning,which consists of color correction and sharpness enhancement algorithms.The color correction method for underwater images proposed in this paper is mainly based on histogram stretching,which corrects the color cast of the image and adjusts the image contrast through the nature of underwater light attenuation and the distribution characteristics of the image histogram.In this paper,a gated fusion network is used to enhance the texture details and image sharpness of the underwater image.The network is a two-branch architecture,which includes four modules:deblurring,super-feature extraction,gated fusion,and texture reconstruction.Experimental results show that the frame has certain effects on improving the white balance of underwater images and enhancing the sharpness of edges.