Image Super-Resolution with Dense-Parallel Swin Transformer

Recent advancements in Swin Transformer-based image super-resolution (SR) methods have demonstrated remarkable progress through efficient modeling capabilities and feature extraction. However, existing approaches still have limitations: in hierarchical feature representation architectures, inter-layer feature propagation efficiency progressively degrades with increasing network depth. For this, the paper proposes an SR model, namely, SwinDPSR, composed of Dense-Parallel Swin Transformer (DPST) blocks. The proposed method innovatively constructs a densely connected parallel Swin Transformer architecture that enhances information flow through multi-level feature reuse mechanisms. Simultaneously, we introduce the Spatial Frequency Block (SFB) that establishes global contextual correlations in the frequency domain branch to precisely compensate for local detail loss. Experiments demonstrate significant improvements over the SwinIR across five benchmarks (Set5, Set14, BSD100, Urban100, and Manga109), achieving an average PSNR gain of 0.28[Formula: see text]dB in [Formula: see text] SR tasks. Theoretical analysis and experimental validation confirm the effectiveness of the parallel architecture, which improves FPS by 6.8% through enhanced computational parallelism. Ablation studies verify the synergistic optimization effect of dense connections and SFB.