Asymmetric Invertible Disentanglement for Image Deraining

Occluded by rain streaks and haze, rainy images suffer from information loss, which necessitates precise recovery of missing content. However, visually appealing yet spurious image details generated by some deraining methods may reduce image fidelity and induce misjudgments in subsequent detection and recognition tasks. Invertible Neural Networks (INNs) are inherently well-suited for the image deraining task. Benefiting from their invertible mapping property, INNs guarantee that all newly generated details can be traced back to the observed input images, thereby improving the fidelity of recovered images. However, it is challenging for INNs to directly map a rainy image to its clean version. Because the invertible mapping requires that the input and the output contain equivalent information, a rainy image inherently loses some details which may lead to information loss in the restored image. In this paper, we propose an Asymmetric Invertible Disentanglement (AInvDis) Network based on a dual-path INN to decompose a rainy image into a rain-free one and a rain map. AInvDis additionally introduces compensated image details generated by a CNN-based module, Prior Compensation Network (PCN), to assist the dual-path INN in restoring background details obscured by rain streaks. Although PCN makes the overall framework no longer strictly invertible, it adaptively extracts relevant details from the observed image based on the feedback of the INN. Moreover, AInvDis's architecture constrains PCN to focus on the generation of missing details, which effectively prevents AInvDis from over-relying on the CNN model that has superior mapping capabilities to restore the entire image, thereby ensuring that the restored image details are maximally preserved through the INN transformation while the missing details are compensated by PCN. Experimental results on both synthetic and real-world datasets demonstrate that our proposed AInvDis not only outperforms recent deraining methods in traditional image assessment metrics but also achieves better perceptual quality on metrics PI and BRISQUE.