Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images
<|reference_start|>Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images: The identification and restoration of ancient watermarks have long been a major topic in codicology and history. Classifying historical documents based on watermarks is challenging due to their diversity, noisy samples, multiple representation modes, and minor distinctions between classes and intra-class variations. This paper proposes a modified U-net-based conditional generative adversarial network (GAN) named Npix2Cpix to translate noisy raw historical watermarked images into clean, handwriting-free watermarked images by performing image translation from degraded (noisy) pixels to clean pixels. Using image-to-image translation and adversarial learning, the network creates clutter-free images for watermark restoration and categorization. The generator and discriminator of the proposed GAN are trained using two separate loss functions, each based on the distance between images, to learn the mapping from the input noisy image to the output clean image. After using the proposed GAN to pre-process noisy watermarked images, Siamese-based one-shot learning is employed for watermark classification. Experimental results on a large-scale historical watermark dataset demonstrate that cleaning the noisy watermarked images can help to achieve high one-shot classification accuracy. The qualitative and quantitative evaluation of the retrieved watermarked image highlights the effectiveness of the proposed approach.<|reference_end|>
@article{saha2024npix2cpix:,
    title={Npix2Cpix: A GAN-Based Image-to-Image Translation Network With
  Retrieval- Classification Integration for Watermark Retrieval From Historical
  Document Images},
    author={Utsab Saha, Sawradip Saha, Shaikh Anowarul Fattah, and Mohammad Saquib},
    journal={IEEE Access 12 (2024) 95857-95870},
    year={2024},
    doi={10.1109/ACCESS.2024.3424662},
    archivePrefix={arXiv},
    eprint={2406.03556},
    primaryClass={cs.CV}
}