IEEE Signal Processing Magazine - January 2018 - 20

DEEP LEARNING FOR VISUAL UNDERSTANDING:
PART 2

Alice Lucas, Michael Iliadis, Rafael Molina,
and Aggelos K. Katsaggelos

Using Deep Neural Networks
for Inverse Problems in Imaging
Beyond analytical methods

T

raditionally, analytical methods have been used to solve
imaging problems such as image restoration, inpainting, and
superresolution (SR). In recent years, the fields of machine
and deep learning have gained a lot of momentum in solving
such imaging problems, often surpassing the performance provided by analytical approaches. Unlike analytical methods for
which the problem is explicitly defined and domain-knowledge
carefully engineered into the solution, deep neural networks
(DNNs) do not benefit from such prior knowledge and instead
make use of large data sets to learn the unknown solution to
the inverse problem. In this article, we review deep-learning
techniques for solving such inverse problems in imaging. More
specifically, we review the popular neural network architectures
used for imaging tasks, offering some insight as to how these
deep-learning tools can solve the inverse problem. Furthermore,
we address some fundamental questions, such as how deeplearning and analytical methods can be combined to provide
better solutions to the inverse problem in addition to providing
a discussion on the current limitations and future directions of
the use of deep learning for solving inverse problem in imaging.

Introduction

©ISTOCKPHOTO.COM/ZAPP2PHOTO

Digital Object Identifier 10.1109/MSP.2017.2760358
Date of publication: 9 January 2018

20

In most applications, an observed signal y can be modeled as
the output of a system T, whose input is denoted by x. Both the
input and output of the system might represent multidimensional signals in general, at one or multiple time and spatial instances. For example, x and y might represent two-dimensional
(2-D) images, or x might consist of multiple video frames and
y be a motion vector field, or x might represent a three-dimensional volume and y a set of 2-D (projection) images.
There are various ways to describe the system T, through,
for example, a differential or difference equation, an integral
equation, or a general mathematical mapping. Such a system T
might model defocusing introduced by the imaging device, blur
due to motion or atmospheric turbulence, an acquisition mask
in a compressive sampling application, and loss in general of
spatiotemporal or spectral information. It might also model the
motion estimation process or the image edge detection process

IEEE SIGNAL PROCESSING MAGAZINE

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January 2018

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1053-5888/18©2018IEEE



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Table of Contents for the Digital Edition of IEEE Signal Processing Magazine - January 2018

Contents
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