Efficient Frequency-Domain Image Deraining with Contrastive Regularization

Most current single image-deraining (SID) methods are based on the Transformer, which brings global modeling capabilities and is critical for high-quality reconstruction. However, their architectures only consider constructing long-range dependencies from the spatial domain, which suffers from a significant computational burden to keep effectiveness. Besides, these methods either overlook negative sample information in the optimization pipeline or underutilize the rain streak characteristics present in the negative ones. To tackle these problems, we propose a Frequency-Aware Deraining Transformer Framework (FADformer) that fully captures frequency domain features to achieve efficient rain removal. Specifically, we first construct the FADBlock, which comprises the Fused Fourier Convolution Mixer (FFCM) and Prior-Gated Feed-forward Network (PGFN). Unlike self-attention mechanisms, the FFCM exclusively conducts convolution operations in both spatial and frequency domains, endowing it with local-global capturing capabilities and efficiency. Simultaneously, the PGFN introduces residue channel prior in a gate-controlled manner to both enhance local details and retain the structure of features. Furthermore, we introduce a Frequency-domain Contrastive Regularization (FCR) during the training phase. The FCR facilitates contrastive learning in the frequency domain, enhancing the contribution of rain streak patterns in negative samples to improve performance. Extensive experiments on synthetic and real-world datasets show that the proposed method significantly outperforms the state-of-the-art approaches. We will release the code soon after the paper is accepted.