Signal Processing - September 2016 - 37

(a) Images Taken at Two Different Exposures

(b) Full Stack of Images and HDR Result

Figure 1. (a) Images captured by standard digital cameras cannot reproduce the wide range of illumination we see in everyday scenes, even after adjusting the exposure, as illustrated by these two images taken at different exposures. (b) HDR imaging allows for the capture of a wider range of illumination;
here, a stack of images was captured at different exposures (left) and merged with the algorithm described in [1] to reduce motion artifacts and produce
the result shown on the right.

developing HDR imaging algorithms and systems to allow
better photographs to be captured.
In this article, we describe research within the computational photography community on HDR imaging that enables
the capture of a wider range of illumination than is normally
captured and produces images closer to what we see with our
own eyes. In a way, HDR imaging represents the epitome of
computational photography: many of the solutions involved
require novel optics, new acquisition processes, and clever
algorithms in the back end to produce better images. As such,
this article will focus only on the acquisition of HDR images
and will not discuss related topics that have been extensively
studied such as HDR image representation (how to compress
and store HDR images) or tone mapping (turning an HDR
image into an LDR image suitable for standard display) [2].
Further, because of this tutorial's strict space limitations, we
cannot cover in depth the large body of existing work on HDR
imaging and refer interested readers instead to textbooks and
papers that survey the subject [1]-[6].

Historical background
As early as the mid-1800s-soon after the invention of photography itself-early photography pioneers were already
struggling with the limited dynamic range of film and began
to develop techniques that provided the basis of what we now
know as HDR imaging. The French photographer Hippolyte
Bayard was the first to propose that two negatives, each one
properly exposed for different content, could be combined to
create a well-balanced photograph. His compatriot Gustave
Le Gray captured many beautiful seascape photographs with
his ciel rapporté technique, where one negative was used for
the dark sea and the other for the bright sky. Others, such as
Oscar Rejlander, combined many well-exposed negatives to
produce photographs that emulated contemporary paintings
in which everything was properly "exposed" (Figure 2).
This idea of combining images acquired with different
exposures to produce an HDR result was reintroduced for

digital photography in the 1990s (almost 150 years later) by
Madden [7] and Mann and Picard [8]. However, HDR imaging received relatively little attention until the seminal paper
by Debevec and Malik [9] placed it at the forefront of the burgeoning computational photography community. Since then,
there has been almost 20 years of research on HDR imaging. Before we delve into this research, however, we must
first review the standard imaging pipeline and understand the
reasons for its limited dynamic range. In addition, we need to
formalize colloquial terms such as brightness by introducing
the appropriate radiometric units that characterize light.

The standard imaging pipeline
and its limited dynamic range
The standard imaging pipeline (Figure 3) starts with a set of
rays leaving the scene in the direction of the camera, with
each ray carrying some amount of radiant power called radiance (L; units: W/m 2 sr) . The rays entering the lens aperture
and striking the sensor at a point are integrated over the solid
angle subtended by the aperture (thereby integrating away the
steradian sr term), resulting in a radiant power density at the

Figure 2. Two Ways of Life, Oscar Gustave Rejlander, 1857. This is one of

the earliest examples of combination printing, in which differently exposed
negatives are combined to extend the dynamic range of the final result. In
this case, 32 negatives were combined to complete the final image. (Image
in the public domain.)

IEEE SIgnal ProcESSIng MagazInE

September 2016

Table of Contents for the Digital Edition of Signal Processing - September 2016