# U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

arXiv: https://arxiv.org/abs/1807.10993 · HTML: https://mlsanity.com/paper/1807.10993/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder](https://mlsanity.com/paper/2307.12255/) (2023)
- [Image Compressed Sensing with Multi-scale Dilated Convolutional Neural Network](https://mlsanity.com/paper/2209.13761/) (2022)

## Abstract (arXiv)

This paper studies the challenging problem of fingerprint image denoising and inpainting. To tackle the challenge of suppressing complicated artifacts (blur, brightness, contrast, elastic transformation, occlusion, scratch, resolution, rotation, and so on) while preserving fine textures, we develop a multi-scale convolutional network, termed U- Finger. Based on the domain expertise, we show that the usage of dilated convolutions as well as the removal of padding have important positive impacts on the final restoration performance, in addition to multi-scale cascaded feature modules. Our model achieves the overall ranking of No.2 in the ECCV 2018 Chalearn LAP Inpainting Competition Track 3 (Fingerprint Denoising and Inpainting). Among all participating teams, we obtain the MSE of 0.0231 (rank 2), PSNR 16.9688 dB (rank 2), and SSIM 0.8093 (rank 3) on the hold-out testing set.