Underwater Image Enhancement Based on Multi-Scale Spatial and Channel Attention Fusion

Abstract

Degraded underwater image enhancement is a challenging task. Due to the light scattering and absorption of suspended particles in water, the original underwater image has a low definition, blurred details, and color distortion, thus affecting advanced underwater visual tasks such as underwater target detection. However, the existing methods are difficult to enhance the image details effectively. In this paper, we proposed an effective deep convolutional neural network model of multi-scale spatial and channel attention fusion (MS-SCANet) for underwater image enhancement. First, a new training data set (UIRDs) is constructed from the existing data. Then, a multi-loss function is constructed to enhance the detail of the image. Finally, the performance of the model in image visibility and color correction is discussed. Through experiments and comparative analysis on two test sets, our method is superior to the existing traditional methods and deep learning models in terms of image visibility, detail enhancement, and color correction.