Abstract: Natural environment factors such as turbulence and suspended particles in water imaging can causeimage distortion,low resolution,and fuzzy background of underwater acquisition.In order to solve the above problems and further improve the quality of image reconstruction and rehabilitation,this paper puts forward an improved image super-resolution reconstruction based on residual network method.This method will in residual dense network of fusion and adaptive mechanism,effectively solve the deep learning gradient explosion problems often encountered in network,also can inhibit learning of useless information,make full use of the important feature information.In order to adapt the network to the underwater noise environment,a self-built underwater system is used to collect the target plate in clear water and turbid micro-turbulent waters respectively,and the training pair of image generation is performed on the target plate,and the test set of image generation is collected under rivers and ocean waters.The experimental results show that in the micro-turbulent ocean and river waters,compared with the traditional underwater image processing and neural network algorithm,the improved residual network algorithm can reconstruct the underwater ima-ge very well.

Key words: Adaptive mechanism, Deep learning, Residual network, Superresolution reconstruction, Underwater image processing