Reflectance-aware structure-preserving multi-image stitching for high-fidelity digitization of Tibetan color paintings

Tibetan color paintings are difficult to digitize in museums because mineral pigments and protective glass often introduce specular reflection, while single overview images lack fine spatial details. We propose a reflectance-aware structure-preserving multi-image stitching framework for high-fidelity digitization. The framework constructs an overlap graph for global alignment, uses HHSA-RDNet to predict dense reflectance probability maps, and incorporates reflectance reliability into matching, seam estimation, and fusion. Motion-guided local refinement further reduces micro-misalignments and preserves decorative structures. We construct TCP-MultiStitch with 780 stitching groups and 9,340 local images. Our method achieves 37.06 dB PSNR, 0.910 SSIM, 0.163 LPIPS, 32.74 dB M-PSNR, and 0.853 M-SSIM on TCP-MultiStitch. Direct evaluation on MuralDH without retraining or fine-tuning achieves 36.49 dB PSNR and 0.903 SSIM, showing promising transfer under the stated protocol, although evaluation is currently limited to one external dataset.