Super-Resolution Reconstruction Algorithm for Underwater Images Based on Deep Residual Learning Networks

Subject to intricate lighting conditions and environmental nuances, including suspended particles, underwater images often grapple with color distortion, background blurriness, and limited resolution. To cope with these challenges and elevate the quality of underwater image reconstruction and restoration, we introduce a super-resolution reconstruction algorithm for underwater images, leveraging a Very Deep Super-Resolution (VDSR) network enrich ed with deep residual learning techniques, namely, Very Deep Residual SuperResolution Network (VDRSRN). This methodology endeavors to meticulously fit the intricate features of the underwater milieu, forge deeper mapping associations, and attain superior model generalization. By integrating residual block structures in tandem with an alternating connection strategy, we amplify the network's depth, thereby augmenting its proficiency in portraying intricate underwater scenes. Additionally, this approach effectively counters the pervasive issues of gradient vanishing and model degradation inherent in deep networks, facilitated by residual learning mechanisms. The alternating configuration of identity residual blocks and convolutional residual blocks retains the stability benefits of identity connections while fostering efficient feature reuse and transmission, significantly mitigating the loss of low-level features within deep networks. Experimental results demonstrate that the proposed algorithm outperforms comparative algorithms in both subjective and objective evaluations, with substantial preservation of texture, edges, and other details in reconstructed underwater images.