  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.
