We propose a novel model‐free unsupervised learning paradigm to tackle the unfavorable prevailing problem of real‐world image deraining, dubbed MUL‐Derain. Beyond existing unsupervised deraining efforts, MUL‐Derain leverages a model‐free Multiscale Attentive Filtering (MSAF) to handle multiscale rain streaks. Therefore, formulation of any rain imaging is not necessary, and it requires neither iterative optimization nor progressive refinement operations. Meanwhile, MUL‐Derain can efficiently compute spatial coherence and global interactions by modeling long‐range dependencies, allowing MSAF to learn useful knowledge from a larger or even global rain region. Furthermore, we formulate a novel multiloss function to constrain MUL‐Derain to preserve both color and structure information from the rainy images. Extensive experiments on both synthetic and real‐world datasets demonstrate that our MUL‐Derain obtains state‐of‐the‐art performance over un/semisupervised methods and exhibits competitive advantages over the fully‐supervised ones.

Object detection remains challenging in hazy weather conditions due to the poor visibility of captured images. There are currently two types of detectors capable of adapting to varying weather conditions: (i) low‐level adaptation methods that combine one detector with an additional dehazing network and (ii) high‐level adaptation methods that explore various kinds of domain adaptation knowledge. However, neither of these approaches can achieve desirable performance due to their inherent limitations. We raise an intriguing question—if combining both low‐level adaptation and high‐level adaptation, can improve the generalization ability of a detector in hazy weather conditions? To answer it, we propose a Joint High‐Low Adaptation Object Detection paradigm (HLA‐HOD) in hazy weather conditions. By combining both low‐level adaptation and high‐level adaptation, HLA‐HOD achieves superior performance on hazy images without requiring ground‐truth bounding boxes or clean images. Extensive experiments demonstrate that our method outperforms state‐of‐the‐art low‐level and high‐level adaptation methods by a large margin both quantitatively and qualitatively.