Rainfall, as a ubiquitous atmospheric phenomenon, causes visual image degradation through rain streaks, which in turn hinders human vision and degrades the performance of subsequent computer vision algorithms. In order to further improve the performance of visual algorithms in harsh rain environments, image rain removal technology has become a hot topic in this field. In computer vision, the integration of the Vision Transformer (ViT) marks an important progress, and its transfer application to the image rain removal task has achieved remarkable success. However, the application of transformers in this task still faces challenges. While they excel at modelling long-range dependencies, transformers are less adept at modelling local contexts for image restoration tasks. Moreover, the internal transmission of features within dense transformer layers often needs more finesse. In this paper, we tackle these shortcomings by incorporating convolutional mechanisms within both the self-attention components of the transformer blocks and the subsequent feed-forward networks. We refine the feature flows amalgamated between recursive transformer blocks by utilizing feature fusion strategies. To further refine the high-frequency textural details of images, we employ frequency domain contrastive learning techniques to augment the delineation of contrastive sample information, ensuring that the restored image closely resembles the clear image in terms of texture structure while maintaining a distinction from the rain image. Extensive quantitative and qualitative experimental results show that the proposed deraining network surpasses mainstream methods on public datasets.