DESSM: Dual Encoder-Based State Space Model for Image Inpainting

Image inpainting represents a fundamental and challenging problem in computer vision, requiring the synthesis of visually plausible content for missing regions while preserving both textural details and structural coherence. While current approaches employ auxiliary networks and attention mechanisms to capture structural priors and expand receptive fields, they remain constrained by two fundamental limitations: (1) insufficient interaction between textural and structural priors, and (2) the quadratic computational complexity inherent in attention operations. To overcome these challenges, we present DESSM, an innovative Dual Encoder-based State space Model for image inpainting that achieves efficient global context modeling with linear computational complexity. Our DESSM integrates three synergistically designed modules: (1) a dual-branch encoder for complementary learning of textural patterns and structural priors, (2) a Feature Cross Fusion Block (FCFB) enabling dynamic feature interaction while adaptively suppressing redundant information, and (3) a Spatial-Channel joint Selective scan Block (SCSB) for efficient long-range dependency modeling. Comprehensive evaluations across four standard benchmarks (i.e., CelebA, CelebA-HQ, Places2, and Paris StreetView) demonstrate that our DESSM achieves state-of-the-art performance in both visual fidelity and computational efficiency.