Benefiting from the advances in object detection in remote sensing, detecting objects in images captured by drones has achieved promising performance in recent years. However, drone-view object detection in rainy weather conditions (Rainy DroneDet) remains a challenge, as small-sized objects blurred by rain streaks offer a little valuable information for robust detection. In this paper, we propose a Collaborative Deraining Network called “CoDerainNet”, which simultaneously and interactively trains a deraining subnetwork and a droneDet subnetwork to improve the accuracy of Rainy DroneDet. Furthermore, we propose a Collaborative Teaching paradigm called “ColTeaching”, which leverages rain-free features extracted by the Deraining Subnetwork and teaches the DroneDet Subnetwork such features, to remove rain-specific interference in features for DroneDet. Due to the lack of an existing dataset for Rainy DroneDet, we built three drone datasets, including two synthetic datasets, namely RainVisdrone and RainUAVDT, and one real drone dataset, called RainDrone. Extensive experiment results on the three rainy datasets show that CoDerainNet can significantly reduce the computational costs of state-of-the-art (SOTA) object detectors while maintaining detection performance comparable to these SOTA models.

Low‐light image enhancement is rapidly gaining research attention due to the increasing demands of extreme visual tasks in various applications. Although numerous methods exist to enhance image qualities in low light, it is still undetermined how to trade‐off between the human observation and computer vision processing. In this work, an effective generative adversarial network structure is proposed comprising both the densely residual block (DRB) and the enhancing block (EB) for low‐light image enhancement. Specifically, the proposed end‐to‐end image enhancement method, consisting of a generator and a discriminator, is trained using the hyper loss function. The DRB adopts the residual and dense skip connections to connect and enhance the features extracted from different depths in the network while the EB receives unique multi‐scale features to ensure feature diversity. Additionally, increasing the feature sizes allows the discriminator to further distinguish between fake and real images from the patch levels. The merits of the loss function are also studied to recover both contextual and local details. Extensive experimental results show that our method is capable of dealing with extremely low‐light scenes and the realistic feature generator outperforms several state‐of‐the‐art methods in a number of qualitative and quantitative evaluation tests.

Underwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of underwater light propagation and the inherent homogeneity of aquatic environments. Images captured underwater are significantly degraded through wavelength-dependent absorption and scattering processes, resulting in color distortion, contrast degradation, and illumination irregularities. To address these challenges, we propose a contrastive feature disentanglement network (CFD-Net) that systematically addresses underwater image degradation. Our framework employs a multi-stream decomposition architecture with three specialized decoders to disentangle the latent feature space into components associated with degradation and those representing high-quality features. We incorporate hierarchical contrastive learning mechanisms to establish clear relationships between standard and degraded feature spaces, emphasizing intra-layer similarity and inter-layer exclusivity. Through the synergistic utilization of internal feature consistency and cross-component distinctiveness, our framework achieves robust feature extraction without explicit supervision. Compared to existing methods, our approach achieves a 12% higher UIQM score on the EUVP dataset and outperforms other state-of-the-art techniques on various evaluation metrics such as UCIQE, MUSIQ, and NIQE, both quantitatively and qualitatively.

Underwater Image Enhancement (UIE) encounters substantial challenges due to the intricate nature of physical degradation processes that diminish visibility in underwater images. Existing methods leverage learning-based models to delineate pixel mappings for either paired or unpaired images to ameliorate the quality of degraded visuals. Nonetheless, the paucity of paired image datasets and the erratic nature of unsupervised learning methods considerably hinder advancements in UIE. This study presents an innovative contrastive learning framework specifically designed for UIE, aimed at effectively addressing the aforementioned challenges. Our strategy reconceptualizes image enhancement as a multi-task joint learning problem, thus fortifying the enhancement process. We pinpoint three pivotal aspects for UIE: contrastive feature learning, semantic information coherence, and cross-domain feature transfer. These elements are imperative for augmenting contrast, preserving texture integrity, and ensuring color accuracy. The contrastive learning paradigm empowers the enhancement module to discern between unpaired positive (high-quality) and negative (degraded) underwater images, facilitating semantic learning in refining the enhancement network systematically. By leveraging the semantic feature domain extracted from unpaired high-quality images, our method demonstrates superior performance, validated by several quality metrics, outperforming recent advancements in unsupervised UIE techniques.