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HCLR-Net: Hybrid Contrastive Learning Regularization With Locally Randomized Perturbation For Underwater Image Enhancement by abhinavrajputmc24b04007 is a document available to read on EtoBox.

What is HCLR-Net: Hybrid Contrastive Learning Regularization With Locally Randomized Perturbation For Underwater Image Enhancement about?

The document presents HCLR-Net, a novel approach for underwater image enhancement that utilizes Hybrid Contrastive Learning Regularization to improve model generalization without relying on paired training data. The method incorporates locally randomized perturbations to create negative samples and features an Adaptive Hybrid Attention module for effective feature extraction. Experimental results demonstrate significant improvements in visual quality and quantitative metrics compared to state-of-the-art met

Author

abhinavrajputmc24b04007

Language

EN