Research On Image Enhancement Algorithms For Underwater Environment

With the scarcity of land resources and the rapid growth of the global population,the rich biological and mineral resources in the ocean have become the target of human exploration.Underwater images play an important role in exploring ocean resources,and can provide visual information of objects in the underwater environment,which helps to better understand the distribution of resources.However,due to the complexity of the underwater environment,light is absorbed and scattered during its propagation in water,resulting in degradation phenomena such as color distortion,low resolution,low contrast,and foggy blurring in underwater images.This incomplete acquisition of visual information in underwater images limits image tasks such as ocean resource exploration and target recognition.Therefore,enhancing underwater images and obtaining high-quality underwater images are of great significance for exploring ocean resources.This article focuses on the research of underwater image enhancement algorithms,and the main contents are as follows:(1)In response to the phenomenon of color distortion and varying degrees of foggy blurring in different areas of underwater images,this paper proposes an underwater image enhancement algorithm based on color correction and regional de-fogging.Firstly,to solve the problem of severe distortion,this paper designs a red channel compensation method and a dynamic range stretching method for color correction.Secondly,in order to efficiently remove the foggy blurring in the image,which cannot be achieved by traditional de-fogging algorithms,this paper uses a region partitioning method to divide the image into foreground and background regions,and then selectively uses de-fogging and detail enhancement methods to process the basic and detail layers of each region.Finally,the enhanced images of each region are combined to obtain the final output image.Experimental results show that the proposed algorithm has good enhancement effects on different types of underwater images.(2)Underwater images usually suffer from multiple degradation phenomena,and current deep learning-based underwater image enhancement algorithms often only design neural network models for a single degradation phenomenon.To address this problem,this paper proposes an underwater image enhancement algorithm based on a dual-channel feature network model.Firstly,in order to solve the problems of color distortion,low resolution,and texture blurring in underwater images,this paper decomposes the images into basic layer images and detail layer images,and then designs corresponding enhancement networks for the basic and detail layers to solve multiple degradation problems.Then,in order to obtain more natural colors in underwater images,this paper designs an optimization network to further improve the color restoration of the preliminarily enhanced underwater images.Compared and analyzed with other mainstream algorithms,the proposed algorithm mainly solves the problem of color distortion in images,as well as other issues such as low clarity,low contrast,and blurry texture details.