DIRBW-Net: An Improved Inverted Residual Network Model for Underwater Image Enhancement

Abstract

Underwater photography is challenged by optical distortions caused by water's absorption and scattering phenomena. These distortions manifest as color aberrations, image blurring, and reduced contrast in underwater scenes. To address these issues, we propose an underwater image enhancement model leveraging an inverted residual network. Firstly, the traditional inverted residual network is improved. In order to minimize the interference of the Batch Normalization (BN) layer on color information, a novel Double-layer Inverted Residual Block (DIRB) is introduced, which omitted the BN layer and extracted deep feature information from the input image. Subsequently, the input image is fused with the intermediate feature map using a skip connection to ensure the consistency between local and global image information, thus effectively enhancing the image quality. In the concluding phase, we delved into the effects of diverse activation functions on the network model's performance, opting for the h-swish activation function to further boost the overall performance. The model underwent evaluation and testing on a public dataset, with comparisons drawn against representative enhancement models. The experiments showcase a notable success in enhancing the underwater image quality when using the proposed model.INDEX TERMS underwater image enhancement, convolutional neural network (CNN), residual network, deep learning.