A Wavelet Based Deep Learning Method for Underwater Image Super Resolution Reconstruction

In order to solve the scattering degradation by turbulence and suspended particles in underwater imaging, traditional processing methods including image enhancement, restoration and reconstruction have been continuously researched. But most of them rely on degradation models, and there exist problems of ill-posed. Image super resolution reconstruction based on deep learning has become a hot topic in recent years. In order to further improve the effectiveness and efficiency of deep learning based methods, an improved image super-resolution reconstruction algorithm based on deep convolutional neural network is proposed in this paper. The wavelet basis which can effectively simulate the waveform and characteristics of underwater turbulence is selected to replace the neuron fitting function in order to improve the accuracy and efficiency of the algorithm. An improved dense block structure (IDB) is introduced into the network which can effectively solve the gradient disappearance problem of deep convolutional neural network and improve the training speed at the same time. The method proposed in this paper has been verified in laboratory flume, public data set and real water body. The experimental results show that under the same conditions, the proposed algorithm shows improvements on various evaluation parameters compared with DRFN, VDSR and DRCN method. So it can be concluded that the proposed method can effectively improve the quality of deep learning based reconstruction for imaging in natural water.