FDWST: Fingerphoto Deblurring using Wavelet Style Transfer

TL;DR

This paper introduces FDWST, a novel fingerphoto deblurring method combining wavelet transforms and style transfer to produce sharper images with high accuracy, outperforming existing techniques.

Contribution

The paper proposes a new deblurring architecture that integrates wavelet transforms with style transfer, enhancing image sharpness and quality beyond prior methods.

Findings

Significantly improved fingerphoto sharpness and quality.

Achieved a peak matching accuracy of 0.9907.

Outperformed multiple state-of-the-art deblurring techniques.

Abstract

The challenge of deblurring fingerphoto images, or generating a sharp fingerphoto from a given blurry one, is a significant problem in the realm of computer vision. To address this problem, we propose a fingerphoto deblurring architecture referred to as Fingerphoto Deblurring using Wavelet Style Transfer (FDWST), which aims to utilize the information transmission of Style Transfer techniques to deblur fingerphotos. Additionally, we incorporate the Discrete Wavelet Transform (DWT) for its ability to split images into different frequency bands. By combining these two techniques, we can perform Style Transfer over a wide array of wavelet frequency bands, thereby increasing the quality and variety of sharpness information transferred from sharp to blurry images. Using this technique, our model was able to drastically increase the quality of the generated fingerphotos compared to their originals, and achieve a peak matching accuracy of 0.9907 when tasked with matching a deblurred fingerphoto to its sharp counterpart, outperforming multiple other state-of-the-art deblurring and style transfer techniques.