Multiscale Image Enhancement and Visualization Analysis for Historical Map/s

Based on the degradation of historical maps, such as fading, stains, creases, blurred lines, and text adhesion, multi-scale image enhancement and spatial element visualization analysis are studied. This study constructs a technical process consisting of sample division, image preprocessing, multi-scale enhancement, quality evaluation, and spatial feature extraction, and further evaluates map registration, vectorization, optical character recognition, and semantic segmentation on the same test maps. The results show that the PSNR, SSIM, EPI, and RCS of the multi-scale enhancement model are 27.94 dB, 0.842, 0.817, and 0.81, respectively, and the comprehensive quality score is 0.872. The road connectivity, water boundary matching, and administrative boundary topology consistency rates reach 87.9%, 85.4%, and 82.6%, while place name and map symbol recognition rates increase to 84.7% and 82.3%. Downstream evaluation shows that registration RMSE decreases from 8.31 to 4.12 px, vectorization F1 increases from 0.713 to 0.871, OCR character accuracy increases from 62.4% to 84.7%, and semantic-segmentation mIoU increases from 0.614 to 0.802. These results indicate that the enhancement improves both visual quality and the reliability of GIS-oriented spatial processing.

@article{Ren2026,
  title     = {Multiscale Image Enhancement and Visualization Analysis for Historical Map/s},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {7},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Guilei Ren},
  doi       = {10.14569/IJACSA.2026.0170765},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170765}
}