Image dehazing is an emblematical low-level vision task that aims at restoring haze-free images from haze images. Recently, some methods adopts deep learning techniques to rebuild haze-free images. However, in real-world scenarios, complex degradation of captured images and non-uniform spatial distributions of haze will significantly weaken the generalization ability of these models. Accordingly, we propose a novel Spatial Dual-Branch Attention Dehazing network (SDBAD-Net) based on the Meta-Former paradigm for end-to-end dehazing. Specifically, we firstly design a robust Spatial Dual-Branch Attention (SDBA) module to filter the haze distribution features from different densities, which is suitable for both uniform and non-uniform situations. Secondly, we introduce a Structural Features Supplementary (SFS) module to dynamically fuse the contextual structural features in a nonlinear manner, so as to correct the image distortion caused by the lack of structural details. Finally, the quantitative and qualitative experiments are carried out on two challenging datasets, and the results show that our method outperforms most of state-of-the-art algorithms with fewer parameters and faster speed, especially surpassing FFA-Net with only 50% parameters and 7% computational costs. In addition, we ulteriorly explore its performance on object detection in foggy weather with our model on the challenging Real-world Task-driven Testing Set (RTTS), and the surprising results further prove the robustness and wide-applicability of our method.

Adverse weather conditions such as haze and snowfall can degrade the quality of captured images and affect performance of drone detection. Therefore, it is challenging to locate and identify targets in adverse weather scenarios. In this paper, a novel model called Object Detection in a Foggy Condition with YOLO (ODFC-YOLO) is proposed, which performs image dehazing and object detection jointly by multi-task learning approach. Our model consists of a detection subnet and a dehazing subnet, which can be trained end-to-end to optimize both tasks. Specifically, we propose a Cross-Stage Partial Fusion Decoder (CSP-Decoder) in the dehazing subnet to recover clean features of encoder from complex weather conditions, thereby reducing the feature discrepancy between hazy and clean images, thus enhancing the feature consistency between different tasks. Additionally, to increase the feature modeling and representation capabilities of our network, we also propose an efficient Global Context Enhanced Extraction (GCEE) module to extract beneficial information from blurred images by constructing global feature context long-range dependencies. Furthermore, we propose a Correlation-Aware Aggregated Loss (CAALoss) to average noise patterns and tune gradient magnitudes across different tasks, accordingly implicitly enhancing data diversity and alleviating representation bias. Finally, we verify the advantages of our proposed model on both synthetic and real-world foggy datasets, and our ODFC-YOLO achieves the highest mAP on all datasets while achieving 36 FPS real-time detection speed.

Accurate forecasting of multienergy loads is essential for designing, operating, scheduling, and managing integrated energy systems (IESs). Recent research suggests that transformer models have the potential to improve long-sequence predictions. However, existing transformer models often emphasize capturing temporal dependencies while neglecting crucial dependencies among different variables necessary for multienergy load forecasting. Moreover, transformer models encounter challenges related to quadratic time complexity and significant memory usage, which hinder their direct applicability to tasks involving long-sequence, multienergy load forecasting. To tackle these challenges, we propose a model called DTformer and apply it to the task of multihorizon, multienergy load forecasting in IES. Within DTformer, we employ patch embedding to convert the input multienergy load sequences into a 3-D vector array, preserving both temporal and variable information. Subsequently, we propose the temporal top windowed attention (TWA) module and the dual variable attention module to handle extended temporal dependencies and intervariable dependencies. Importantly, the computational complexity and memory requirements of the TWA model are regulated at a level of $O(N^\frac{4}{3})$ . Through extensive experimentation, we found that our DTformer surpasses baseline models in terms of performance using the IES dataset sourced from Arizona State University's Tempe campus.

Synthetic Aperture Radar (SAR) has attracted significant attention due to its excellent all-weather imaging capabilities. However, SAR image object detection methods face two major challenges: 1) Most existing datasets are small in volume and single in category and scene. 2) Existing IoU-based loss functions cannot fully capture the relationship between prediction and target bounding boxes. To further advance the development of the SAR object detection method, we construct a large-scale multi-scene SAR object detection dataset called MSOD. It comprises three distinct scenarios, containing 40K images and about 1M instances of interest classified into six categories. In addition, we propose a novel diagonal-based similarity loss, Diagonal-Geometry IoU (DGIoU), to optimize the performance of SAR object detection by measuring the similarity between the diagonal of the prediction and target boxes. Specifically, we equivalently represent a rectangular box as a diagonal, and then define DGIoU based on the similarity of a set of sampling points between the diagonals of the predicted box and the target box. DGIoU effectively characterizes the difference between the predicted box and the target box, particularly in box inclusion and separation cases, resulting in improved localization accuracy. Numerous experimental results demonstrate that MSOD is closer to practical application and more challenging than existing SAR image datasets, and serves as a strong benchmark for evaluating the effectiveness of various IoU loss functions. The dataset and code are available at: https://github.com/wchao0601/MSOD-DGIoU.

Unsupervised image restoration under multi-weather conditions remains a fundamental yet underexplored challenge. While existing methods often rely on task-specific physical priors, their narrow focus limits scalability and generalization to diverse real-world weather scenarios. In this work, we propose WeatherCycle, a unified unpaired framework that reformulates weather restoration as a bidirectional degradation-content translation cycle, guided by degradation-aware curriculum regularization. At its core, WeatherCycle employs a lumina-chroma decomposition strategy to decouple degradation from content without modeling complex weather, enabling domain conversion between degraded and clean images. To model diverse and complex degradations, we propose a Lumina Degradation Guidance Module (LDGM), which learns luminance degradation priors from a degraded image pool and injects them into clean images via frequency-domain amplitude modulation, enabling controllable and realistic degradation modeling. Additionally, we incorporate a Difficulty-Aware Contrastive Regularization (DACR) module that identifies hard samples via a CLIP-based classifier and enforces contrastive alignment between hard samples and restored features to enhance semantic consistency and robustness. Extensive experiments across serve multi-weather datasets, demonstrate that our method achieves state-of-the-art performance among unsupervised approaches, with strong generalization to complex weather degradations.