Uncertainty-driven Progressive Single Image De-raining

Over the past years, progressive methods have demonstrated promising performance in single image de-raining task. Nonetheless, current methods still struggle to precisely remove rain and preserve more image details during the progressive de-raining process, resulting in undesirable local artifacts or image detail loss. To tackle these limitations, a novel progressive approach, called Uncertainty-driven Progressive Single Image De-raining (UPSID), is proposed. Firstly, a powerful internal-and-external dense sub-network is devised, which effectively integrates three proper and flexible components, including dense connection, long short-term memory, and channel attention. Subsequently, the sub-network is further unfolded into multiple recurrent stages to form a progressive de-raining network. Finally, the overall progressive de-raining network is trained with an adaptive weighted loss to focus more on challenging pixels that characterize rain or texture/edge regions. Extensive quantitative and qualitative experiments confirm that the proposed UPSID outperforms multiple state-of-the-art algorithms, including single-stage, progressive, and uncertainty-driven single image de-raining methods. Additionally, this paper also demonstrates the superiority of UPSID for other similar image restoration tasks such as single image de-snowing. The code will be publicly available at https://github.com/Lcai-QZ/UPSID.