# ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder

arXiv: https://arxiv.org/abs/2307.12255 · HTML: https://mlsanity.com/paper/2307.12255/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 23 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Deep Learning on Image Denoising: An overview](https://mlsanity.com/paper/1912.13171/) (2019)
- [Sokoto Coventry Fingerprint Dataset](https://mlsanity.com/paper/1807.10609/) (2018)
- [FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks](https://mlsanity.com/paper/1812.10191/) (2018)
- [Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization](https://mlsanity.com/paper/2004.11349/) (2020)
- [Deep Residual Autoencoder for quality independent JPEG restoration](https://mlsanity.com/paper/1903.06117/) (2019)
- [U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting](https://mlsanity.com/paper/1807.10993/) (2018)
- [Measuring Hardware Impairments with Software-Defined Radios](https://mlsanity.com/paper/1810.04376/) (2018)
- [ChaLearn Looking at People: Inpainting and Denoising challenges](https://mlsanity.com/paper/2106.13071/) (2021)
- [AASAE: Augmentation-Augmented Stochastic Autoencoders](https://mlsanity.com/paper/2107.12329/) (2021)

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

- [Cross-Attribute Matrix Factorization Model with Shared User Embedding](https://mlsanity.com/paper/2308.07284/) (2023)
- [Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration](https://mlsanity.com/paper/2308.11644/) (2023)

## Abstract (arXiv)

The utilization of biometric authentication with pattern images is increasingly popular in compact Internet of Things (IoT) devices. However, the reliability of such systems can be compromised by image quality issues, particularly in the presence of high levels of noise. While state-of-the-art deep learning algorithms designed for generic image denoising have shown promise, their large number of parameters and lack of optimization for unique biometric pattern retrieval make them unsuitable for these devices and scenarios. In response to these challenges, this paper proposes a lightweight and robust deep learning architecture, the Residual Wavelet-Conditioned Convolutional Autoencoder (Res-WCAE) with a Kullback-Leibler divergence (KLD) regularization, designed specifically for fingerprint image denoising. Res-WCAE comprises two encoders - an image encoder and a wavelet encoder - and one decoder. Residual connections between the image encoder and decoder are leveraged to preserve fine-grained spatial features, where the bottleneck layer conditioned on the compressed representation of features obtained from the wavelet encoder using approximation and detail subimages in the wavelet-transform domain. The effectiveness of Res-WCAE is evaluated against several state-of-the-art denoising methods, and the experimental results demonstrate that Res-WCAE outperforms these methods, particularly for heavily degraded fingerprint images in the presence of high levels of noise. Overall, Res-WCAE shows promise as a solution to the challenges faced by biometric authentication systems in compact IoT devices.