IPAS-Net: A deep-learning model for generating high-fidelity shoeprints from low-quality images with no natural references

Single image super-resolution (SISR) typically reconstructs higher-resolution (HR) images from the corresponding low-resolution (LR) images in the presence of natural HR images. SISR is highly important in generating high-quality images in forensic scenarios since it facilitates close investigation and examination of captured shoeprints. However, it becomes more challenging when there are no available natural HR ground truth images that correspond to the input LR images. In such cases, HR reconstruction becomes even more crucial for providing HR versions that retain the natural characteristics of shoeprints. For this purpose, we propose IPAS-Net, which utilizes U-Net for feature extraction, shares the learned parameters from LR space in HR space, and innovatively upscales, refines, and enhances the HR space via special treatments. The upsampling-and-refinement block comprises a parallel pipeline composed of attention mechanism block (AMB) and one-step-high-iteration (OSHI). All upsampling solutions are infused so that distinct upscaling can compensate each others’ weaknesses. The generated HR shoeprints are evaluated using blind/non-reference evaluation metrics, and the proposed method outperforms the state of the art (SOTA) deep learning modalities on low-quality shoeprints.