WANG Ning, JIA Wei, CHEN Yanzheng, WEI Yi, WU Haojun
Due to inherent scattering and absorption, underwater images inevitably suffer from multiple degradations arising from blurring, low contrast and color distortion, thereby seriously deteriorating visual perception. In this paper, a deep learning-based underwater image restoration and enhancement framework (UIRENet) was proposed by virtue of depth estimation and gradient descent strategy. With the aid of convolutional and nonlinear activation function modules, a deep perception network was constructed to achieve scene depth perception maps for different degradation regions, thereby overcoming the dependence of scene depth degradation. A gradient optimization strategy was further proposed to optimize the parameters of convolutional networks and improve the performance of deep network enhancement. Combined with perceptual, edge and underwater color constancy losses, a comprehensive loss function for underwater image enhancement networks was rationally formed. Comprehensive experiments on the UIEB-90, UIEB-M and EUVP datasets show that the UIRENet framework significantly outperforms typical underwater image enhancement methods in terms of reducing underwater image blurriness and improving visual effects. In particular, comparing to CLAHE, ICM, GC, IBLA, DCP, ULAP, FUnIE-GAN, UGAN and Uformer, the objective evaluation metric UIQM can be promoted by 0.3700, 0.6446, 0.5919, 1.3081,1.3032, 1.1672, 0.0593, 0.1329 and 0.0934, respectively.
