Solving Inverse Computational Imaging Problems using Deep Pixel-level Prior

TL;DR

This paper introduces a flexible deep generative prior based on autoregressive models for solving various inverse computational imaging problems, outperforming existing methods in reconstruction quality.

Contribution

It proposes using autoregressive deep generative models as a universal prior for inverse imaging, enabling improved reconstructions across multiple systems.

Findings

Outperforms state-of-the-art methods in perceptual quality

Effective on both real and simulated data

Handles global multiplexing in compressive imaging

Abstract

Signal reconstruction is a challenging aspect of computational imaging as it often involves solving ill-posed inverse problems. Recently, deep feed-forward neural networks have led to state-of-the-art results in solving various inverse imaging problems. However, being task specific, these networks have to be learned for each inverse problem. On the other hand, a more flexible approach would be to learn a deep generative model once and then use it as a signal prior for solving various inverse problems. We show that among the various state of the art deep generative models, autoregressive models are especially suitable for our purpose for the following reasons. First, they explicitly model the pixel level dependencies and hence are capable of reconstructing low-level details such as texture patterns and edges better. Second, they provide an explicit expression for the image prior which can then be used for MAP based inference along with the forward model. Third, they can model long range dependencies in images which make them ideal for handling global multiplexing as encountered in various compressive imaging systems. We demonstrate the efficacy of our proposed approach in solving three computational imaging problems: Single Pixel Camera (SPC), LiSens and FlatCam. For both real and simulated cases, we obtain better reconstructions than the state-of-the-art methods in terms of perceptual and quantitative metrics.