Xiang Chen

The key success of existing video super-resolution (VSR) methods stems mainly from exploring spatial and temporal information that is usually achieved by a temporal propagation with alignment strategies. However, inaccurate alignment usually leads to significant artifacts that will be accumulated during propagation and thus affect video restoration. Moreover, only propagating the same timestep features forward or backward does not handle the videos with complex motion or occlusion. To address these issues, we propose a collaborative feedback discriminative (CFD) method to correct inaccurate aligned features and better model spatial and temporal information for VSR. Specifically, we first develop a discriminative alignment correction (DAC) method to reduce the influences of the artifacts caused by inaccurate alignment. Then, we propose a collaborative feedback propagation (CFP) module based on feedback and gating mechanisms to explore spatial and temporal information of different timestep features from forward and backward propagation simultaneously. Finally, we embed the proposed DAC and CFP into commonly used VSR networks to verify the effectiveness of our method. Experimental results demonstrate that our method improves the performance of existing VSR models while maintaining a lower model complexity.

Image deraining plays a pivotal role in low-level computer vision, serving as a prerequisite for robust outdoor surveillance and autonomous driving systems. While deep learning paradigms have achieved remarkable success in firmly aligned settings, they often suffer from severe performance degradation when generalized to unseen Out-of-Distribution (OOD) scenarios. This failure stems primarily from the significant domain discrepancy between synthetic training datasets and the complex physical dynamics of real-world rain. To address these challenges, this paper proposes a pioneering cross-scenario deraining adaptation framework. Diverging from conventional approaches, our method obviates the requirements for paired rainy observations in the target domain, leveraging exclusively rain-free background images. We design a Superpixel Generation (Sup-Gen) module to extract stable structural priors from the source domain using Simple Linear Iterative Clustering. Subsequently, a Resolution-adaptive Fusion strategy is introduced to align these source structures with target backgrounds through texture similarity, ensuring the synthesis of diverse and realistic pseudo-data. Finally, we implement a pseudo-label re-Synthesize mechanism that employs multi-stage noise generation to simulate realistic rain streaks. This framework functions as a versatile plug-and-play module capable of seamless integration into arbitrary deraining architectures. Extensive experiments on state-of-the-art models demonstrate that our approach yields remarkable PSNR gains of up to 32% to 59% in OOD domains while significantly accelerating training convergence.

Nighttime image deraining presents unique challenges compared to daytime scenarios, primarily due to the complex characteristics of nighttime scenes and the limited availability of datasets that effectively capture the interplay between lighting and rain streaks. In this paper, we contribute a high-quality nighttime image deraining dataset, BDD-NightRain, consisting of 5,600 synthetic nighttime rain images with higher realism and harmony for driving scenes. Furthermore, we develop a two-stage Nighttime Rain Removal Network (NRRNet) that leverages color space transformation. Specifically, the coarse processing stage focuses on rain streak removal in the Y channel, where luminance contrast is most pronounced under nighttime rain conditions, while the fine processing stage further refines the deraining results of the previous stage. In addition, we introduce coordinate-based implicit neural representations between these two stages to guide the model's focus toward areas with appropriate illumination. Extensive experiments show that our method outperforms others in removing rain streaks from nighttime scenes, with potential applications in night time object detection. The code and dataset will be available at https://github.com/guanqiyuan/NRRNet.

Despite significant advancements in image deraining, most existing methods are carried out on low-resolution images, leaving their effectiveness on high-resolution images uncertain. This limitation becomes even more pronounced with the rise of ultra-high-definition (UHD) imaging. In this paper, we tackle the challenge of UHD image deraining and introduce 4K-Rain13 k, the first large-scale UHD image deraining dataset, featuring 13,000 paired images at 4 K resolution. Leveraging this dataset, we conduct a benchmark study on existing methods for processing UHD images. To better address this task, we propose UDR-Mixer, an efficient and effective architecture tailored for UHD image deraining. Our model comprises two key components: a spatial feature rearrangement layer, which captures long-range dependencies in UHD images, and a frequency feature modulation layer, which enhances high-fidelity image reconstruction. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods while maintaining lower model complexity. The source code and proposed dataset are available at https://github.com/cschenxiang/UDR-Mixer.

Remote sensing images are frequently affected by adverse weather conditions, such as haze and raindrops, which degrade image quality and subsequently impair the performance of downstream vision tasks. Currently, mainstream methods for remote sensing image restoration primarily rely on Convolutional Neural Networks (CNNs) and Transformer architectures. However, CNNs face limitations in handling long-range dependencies, while Transformers are constrained by computational efficiency, making it difficult to strike a balance between performance and efficiency. To address these issues, we propose a hierarchical state-space model for remote sensing image restoration, termed hierarchical RS Mamba (Hi-RSMamba). This model enhances contextual modeling by integrating multiscale representations through both global and local state-space models. Specifically, we introduce a hierarchical state-space model (HSSM) that improves the original state-space module by incorporating hierarchical feature representations while preserving local 2-D dependencies, thus enabling better aggregation of rich local and global information. Furthermore, given the complementary nature of global and local dependencies, we design a gated feedforward network to adaptively assist the broadcaste of these multiscale features, allowing for high-quality image background reconstruction. Extensive experimental results demonstrate that Hi-RSMamba exhibits significant advantages on widely used benchmark datasets in remote sensing image restoration tasks.