Real-World Image Deraining Using Model-Free Unsupervised Learning

Abstract

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.