Anti-scatter grids enhance image contrast in digital radiography but can introduce gridline artifacts when grid and detector sampling frequencies misalign. We propose a dual-domain AI framework that integrates a frozen DINO Vision Transformer with a FiLM-conditioned U-Net, combining global semantic encoding with frequency-aware reconstruction. The DINO embeddings modulate U-Net activations via Feature-wise Linear Modulation, enabling anatomically consistent correction through the joint prediction of a spatial residual and a frequency-domain attenuation mask, which are adaptively fused to suppress gridline artifacts while preserving tone and structural detail. A physics-based synthetic dataset comprising 1,475 clean and 4,425 grid-contaminated radiographs was generated from real detector captures. On 555 test images with available clean references, the model achieved a mean PSNR of 36.9 dB and an SSIM of 0.98, demonstrating high-fidelity and structurally consistent reconstruction across varying grid frequencies and exposure conditions.