In this paper, we address the problem of video-based rain streak removal by developing an event-aware multi-patch progressive neural network. Rain streaks in video exhibit correlations in both temporal and spatial dimensions. Existing methods have difficulties in modeling the characteristics. Based on the observation, we propose to develop a module encoding events from neuromorphic cameras to facilitate deraining. Events are captured asynchronously at pixel-level only when intensity changes by a margin exceeding a certain threshold. Due to this property, events contain considerable information about moving objects including rain streaks passing though the camera across adjacent frames. Thus we suggest that utilizing it properly facilitates deraining performance non-trivially. In addition, we develop a multi-patch progressive neural network. The multi-patch manner enables various receptive fields by partitioning patches and the progressive learning in different patch levels makes the model emphasize each patch level to a different extent. Extensive experiments show that our method guided by events outperforms the state-of-the-art methods by a large margin in synthetic and real-world datasets.

Multi-modal image fusion (MMIF) aims to integrate complementary information from heterogeneous sensor modalities. However, substantial cross-modality discrepancies hinder joint scene representation and lead to semantic degradation in the fused output. To address this limitation, we propose C²MFuse, a novel framework designed to preserve content while ensuring cross-modality consistency. To the best of our knowledge, this is the first MMIF approach to explicitly disentangle style and content representations across modalities for image fusion. C²MFuse introduces a content-preserving style normalization mechanism that suppresses modality-specific variations while maintaining the underlying scene structure. The normalized features are then progressively aggregated to enhance fine-grained details and improve content completeness. In light of the lack of ground truth and the inherent ambiguity of the fused distribution, we further align the fused representation with a well-defined source modality, thereby enhancing semantic consistency and reducing distributional uncertainty. Additionally, we introduce an adaptive consistency loss with learnable transformation, which provides dynamic, modality-aware supervision by enforcing global consistency across heterogeneous inputs. Extensive experiments on five datasets across three representative MMIF tasks demonstrate that C²MFuse achieves efficient and high-quality fusion, surpasses existing methods, and generalizes effectively to downstream visual applications.

Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have led to significant progress in nighttime image deraining. However, current architectures still struggle to strike an optimal balance between computational efficiency and restoration performance. Moreover, existing methods often fail to fully exploit the intrinsic characteristics of low-light conditions and inadequately model the interaction between rain and illumination. To overcome these challenges, we propose NDMamba, a dual-prior-guided state-space model that addresses nighttime deraining by incorporating degradation cues related to both lighting and rain distribution. Inspired by the Retinex theory, which suggests that rain streak distribution is influenced by the reflectance component of a scene, we propose a Prior Extraction Module (PEM) to jointly model lighting conditions and rain degradation. Furthermore, we design a Prior-Guided Mamba Block (PGMB), which comprises a Lighting-Adaptive Vision State-Space Module (LVSSM) that incorporates illumination priors, and a Rain Distribution Guidance Module (RDGM) to enhance local features in a more refined manner. Extensive experiments demonstrate that NDMamba outperforms state-of-the-art methods on both synthetic and real-world benchmark datasets. Our code is publicly available at https://github.com/tandaily/NDMamba.

Significant progress has been made in video restoration under rainy conditions over the past decade, largely propelled by advancements in deep learning. Nevertheless, existing methods that depend on paired data struggle to generalize effectively to real-world scenarios, primarily due to the disparity between synthetic and authentic rain effects. To address these limitations, we propose a dual-branch spatio-temporal state-space model to enhance rain streak removal in video sequences. Specifically, we design spatial and temporal state-space model layers to extract spatial features and incorporate temporal dependencies across frames, respectively. To improve multi-frame feature fusion, we derive a dynamic stacking filter, which adaptively approximates statistical filters for superior pixel-wise feature refinement. Moreover, we develop a median stacking loss to enable semi-supervised learning by generating pseudo-clean patches based on the sparsity prior of rain. To further explore the capacity of deraining models in supporting other vision-based tasks in rainy environments, we introduce a novel real-world benchmark focused on object detection and tracking in rainy conditions. Our method is extensively evaluated across multiple benchmarks containing numerous synthetic and real-world rainy videos, consistently demonstrating its superiority in quantitative metrics, visual quality, efficiency, and its utility for downstream tasks.