Restoring severely out-of-focus blurred text images with Deep Image Prior

Abstract

Deblurring is a classical image processing problem with several techniques to solve it. However, increasingly complex methods are required as the degradation increases. Meanwhile, Deep Image Prior (DIP) is a technique, based on artificial neural networks, that does not depend on training data and presents promising results in several image processing tasks. In this work, we proposed the combination of DIP with a supervised convolutional neural network to deblur severely out-of-focus blurred text images from the Helsinki Deblur Challenge (HDC2021) dataset. We evaluated the deblurred text images using optical character recognition results and had a satisfactory performance up to the 16th highest blur category. We deblurred natural images of the same dataset to characterize our method as a general-purpose deblurring algorithm, recovering moderate details up to the 13th highest blur category. The experimental results were competitive against other state-of-art methods, showing the potential and robustness of our method. However, its high computational demands may hinder real-time applications.