IN FOCUS- HYBRID DEEP LEARNING APPROACHES TO THE HDC2021 CHALLENGE

In this work, we present our contribution to the Helsinki Deblur Challenge 2021. The goal of the challenge was to recover images of sequences of letters from progressively out-of-focus photographs. While the blur model was unknown, a dataset of sharp and blurry images was provided. We propose to tackle this problem in a two-step process: (i) the blur models are first extracted and estimated from the provided dataset, and (ii) then incorporated into the reconstruction process. Here, we present three different ways of integrating the estimated model into learning-based methods: (i) an educated deep image prior employing the estimated model in the loss function, (ii) a learned iterative approach that directly employs the estimated model in the architecture and (iii) a fully learned approach where we used the estimated model to simulate additional training data. These three models are improved versions of our original contributions to the challenge. We compare and benchmark them on the released test set of the HDC2021.