An image enhancement method for turbid water based on improved shallow-UWnet
Aiming at the problems of image contrast reduction and serious color cast in turbid water,we constructed a dataset of underwater image for experimental turbid water,and proposed an image enhancement method based on improved Shallow-UWnet network.Firstly,we employed the algorithm of gray scale for global color correction to original images.And then we utilized the improved Shallow-UWnet network,which learned the mapping relationship between the distorted and the normal images,to achieve underwater image enhancement.Finally,we improved the contrast of images to obtain final results,by employing contrast limited adaptive histogram equalization(CLAHE).The experimental results show that our method is superior to other 5 ones not only in subjective and objective evaluation indexes but also in key points matching.And it is effectively in correcting the color cast in different turbid water and improving the contrast and clarity.This method can be applied to underwater in-situ environment with turbidity,and is an available solution for improving underwater visualization.It has wide prospect in underwater detection,underwater salvation,underwater exploration and so on.
image processingunderwater image enhancementturbid waterdeep learningimproved Shallow-UWnet network model
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