Diff-RCformer: A diffusion-augmented Recursive Context Transformer for image super-resolution

Diffusion models have recently exhibited strong potential in single-image super-resolution (SISR) by effectively modeling complex data distributions and generating high-quality reconstructions. However, existing diffusion-based SISR methods often suffer from excessive iterative steps, resulting in a high computational overhead and slow convergence. In addition, traditional convolutional neural networks and Transformer-based architectures have difficulty in capturing complex global information, thereby limiting the reconstruction quality. To address these issues, we propose Diff-RCformer, which is a novel SISR framework that integrates diffusion-based prior generation with the Recursive Context Transformer (RCformer) to achieve robust and efficient super-resolution. Specifically, we use the diffusion model to generate high-quality prior features for super-resolution by iteratively refining Gaussian noise in a compressed latent space. These prior features are then injected into the RCformer, guiding it to reconstruct the high-resolution image. In the RCformer, we introduce Prior-Guided Recursive Generalization Network (PG-RGN) blocks. These blocks recursively aggregate the input features into representative feature maps, enabling them to adapt flexibly to input features of different dimensions and extract global information through cross-attention. We also combine the PG-RGN with Prior-Guided Local Self-Attention (PG-LSA) to enable the model to capture local detail features accurately and enhance the utilization of the global context. To achieve an optimal combination of local and global features, we propose Adaptive Feature Integration (AFI), which efficiently fuses local and global features across multiple attention layers. Our method also supports cascaded super-resolution, enabling flexible multi-stage refinement, which is particularly useful for complex scenarios. Comprehensive experiments on standard benchmarks indicate that Diff-RCformer surpasses recent state-of-the-art methods both quantitatively and qualitatively. https://github.com/SureT-T/Diff-RCformer