Underwater image enhancement presents a significant challenge due to the complex and diverse underwater environments that result in severe degradation phenomena such as light absorption, scattering, and color distortion. More importantly, obtaining paired training data for these scenarios is a challenging task, which further hinders the generalization performance of enhancement models. To address these issues, we propose a novel approach, the Hybrid Contrastive Learning Regularization (HCLR-Net). Our method is built upon a distinctive hybrid contrastive learning regularization strategy that incorporates a unique methodology for constructing negative samples. This approach enables the network to develop a more robust sample distribution. Notably, we utilize non-paired data for both positive and negative samples, with negative samples are innovatively reconstructed using local patch perturbations. This strategy overcomes the constraints of relying solely on paired data, boosting the model’s potential for generalization. The HCLR-Net also incorporates an Adaptive Hybrid Attention module and a Detail Repair Branch for effective feature extraction and texture detail restoration, respectively. Comprehensive experiments demonstrate the superiority of our method, which shows substantial improvements over several state-of-the-art methods in terms of quantitative metrics, significantly enhances the visual quality of underwater images, establishing its innovative and practical applicability. Our code is available at: https://github.com/zhoujingchun03/HCLR-Net.

Integrating multispectral data has been demonstrated to be an effective solution for illumination-invariant pedestrian detection, in particular, RGB and thermal images can provide complementary information to handle light variations. However, most of the current multispectral detectors fuse the multimodal features by simple concatenation, without discovering their latent relationships. In this paper, we propose a cross-modal feature learning (CFL) module, based on a split-and-aggregation strategy, to explicitly explore both the shared and modality-specific representations between paired RGB and thermal images. We insert the proposed CFL module into multiple layers of a two-branch-based pedestrian detection network, to learn the cross-modal representations in diverse semantic levels. By introducing a segmentation-based auxiliary task, the multimodal network is trained end-to-end by jointly optimizing a multi-task loss. On the other hand, to alleviate the reliance of existing multispectral pedestrian detectors on thermal images, we propose a knowledge distillation framework to train a student detector, which only receives RGB images as input and distills the cross-modal representations guided by a well-trained multimodal teacher detector. In order to facilitate the cross-modal knowledge distillation, we design different distillation loss functions for the feature, detection and segmentation levels. Experimental results on the public KAIST multispectral pedestrian benchmark validate that the proposed cross-modal representation learning and distillation method achieves robust performance.