Deep-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network

Abstract

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.

arXiv commentAccepted. 27th IEEE International Conference on Image Processing (ICIP), 2020

References in corpus (7)

• Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
• Designing Unimodular Codes via Quadratic Optimization is not Always Hard
• One-Bit Radar Processing with Time-Varying Sampling Thresholds
• Deep Signal Recovery with One-Bit Quantization
• Signal Recovery From 1-Bit Quantized Noisy Samples via Adaptive Thresholding
• Model-Aware Deep Architectures for One-Bit Compressive Variational Autoencoding
• Waveform Design for One-Bit Radar Systems Under Uncertain Interference Statistics

Cited by in corpus (2)

• LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
• Unfolded Algorithms for Deep Phase Retrieval