Two-stage underwater image enhancement method based on convolutional neural networks
Images taken underwater frequently suffer from substantial degradation due to the varied capabilities of water particles to absorb light,which has a significant impact on how underwater robots perceive their surroundings.The intricacy of underwater environments and uncertainties in physical factors usually result in poor generalization for traditional image processing techniques and degradation model-based picture restoration systems.A two-stage underwater image enhancement technique based on convolutional neural networks(CNNs)is suggested to improve the quality of underwater images.This method improves degraded underwater images into visually superior near-air images through damage and restoration phases.Testing results on Challenge60,U45,EUVP,and RUIE datasets show that the proposed method achieves better enhancement compared to existing underwater image restoration and enhancement algorithms,with improvements of 5.18%and 6.64%respectively for UIQM and UCIQE scores.
万方数据
维普
