ResWCAE: Biometric Pattern Image Denoising Using Residual
  Wavelet-Conditioned Autoencoder
Youzhi Liang Wen Liang
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.