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Citation Author(s):
Shahin Khobahi; Arindam Bose; Mojtaba Soltanalian; Dan Schonfeld
Submitted by:
Chirag Agarwal
Last updated:
2 November 2020 - 10:54am
Document Type:
Presentation Slides
Document Year:
Presenters Name:
Chirag Agarwal
Paper Code:



The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications. Hence, it is of paramount importance to develop interpretable deep learning models. In this paper, we consider the problem of blind deconvolution and propose a novel model-aware deep architecture that allows for the recovery of both the blur kernel and the sharp image from the blurred image. In particular, we propose the Deep Unfolded Richardson-Lucy (Deep-URL) framework -- an interpretable deep-learning architecture that can be seen as an amalgamation of classical estimation technique and deep neural network, and consequently leads to improved performance. Our numerical investigations demonstrate significant improvement compared to state-of-the-art algorithms.

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Dataset Files

The final presentation for the video presented at ICIP 2020