The most significant advantage of holographic imaging is that one does not need to do focusing alignment for the scene or objects while capturing their images. To focus on a particular object recorded in a digital hologram, a post-processing on the recorded image must be performed. This post-processing, so called the reconstruction, is essentially the calculation of wave propagation in free space. If the object's optical distance to the recording plane is not known a priori, focusing methods are used to estimate this distance. However, these operations can be quite time consuming as the hologram sizes increase. When there is a time constraint on these procedures and the image resolution is high, traditional central processing units (CPUs) can no longer satisfy the desired reconstruction speeds. Then, especially for real-time operations, additional hardware accelerators are required for reconstructing high resolution holograms. To this extend, today's commercial graphic cards offer a viable solution, as the holograms can be reconstructed tens of times faster with a graphics processing unit than with the state-of-the-art CPUs. Here we present an auto-focusing megapixel-resolution digital holographic microscope (DHM) that uses a graphics processing unit (GPU) as the calculation engine. The computational power of the GPU allows the DHM to work in real-time such that the reconstruction distance is estimated unsupervised, and the post-processing of the holograms are made completely transparent to the user. We compare DHM with GPU and CPU and present experimental results showing a maximum of 70 focused reconstructions per second (frps) with 1024 × 1024 pixel holograms.

1.
U.
Schnars
and
W. P. O.
Jüptner
, “
Digital recording of holograms by a CCD target and numerical reconstruction
,”
Appl. Opt.
33
(
2
),
179
181
(
1994
).
2.
T.
Shimobaba
,
Y.
Sato
,
J.
Miura
,
M.
Takenouchi
, and
T.
Ito
, “
Real-time digital holographic microscopy using the graphic processing unit
,”
Opt. Express
16
,
11776
11781
(
2008
).
3.
L.
Ahrenberg
,
A. J.
Page
,
B. M.
Hennelly
,
J. B.
McDonald
, and
T. J.
Naughton
, “
Using commodity graphics hardware for real-time digital hologram view-reconstruction
,”
J. Disp. Technol.
5
,
111
119
(
2009
).
4.
M.
Özcan
and
M.
Bayraktar
, “
Digital holography image reconstruction methods
,”
Proc. SPIE
7233
,
72330B
(
2009
).
5.
J. W.
Goodman
,
Introduction to Fourier Optics
, 2nd ed. (
McGraw-Hill Companies, Inc.
,
New York
,
1996
).
6.
Y.
Takaki
,
H.
Kawai
, and
H.
Ohzu
, “
Hybrid holographic microscopy free of conjugate and zero-order images
,”
Appl. Opt.
38
,
4990
4996
(
1999
).
7.
P.
Langehanenberg
,
B.
Kemper
,
D.
Dirksen
, and
G.
von Bally
, “
Autofocusing in digital holographic phase contrast microscopy on pure phase objects for live cell imaging
,”
Appl. Opt.
47
(
19
),
D176
D182
(
2008
).
8.
H. A.
İlhan
,
M.
Doğar
, and
M.
Özcan
, “
Fast autofocusing in digital holography using scaled holograms
,”
Opt. Commun.
287
,
81
84
(
2013
).
9.
F. C.
Groen
,
I. T.
Young
, and
G.
Ligthart
, “
A comparison of different focus functions for use in autofocus algorithms
,”
Cytometry
6
,
81
91
(
1985
).
10.
Y.
Sun
,
S.
Duthaler
, and
B. J.
Nelson
, “
Autofocusing in computer microscopy: Selecting the optimal focus algorithm
,”
Microsc. Res. Tech.
65
,
139
149
(
2004
).
11.
NVIDIA Corporation
,
CUDA CUFFT Library
, 2nd ed. (
Nvidia Corporation
,
Santa Clara, CA
,
2010
).
12.
D. B.
Kirk
and
W.
mei W. Hwu
,
Programming Massively Parallel Processors: A Hands-On Approach
, 2nd ed., Applications of GPU Computing Series (
Morgan Kaufmann
,
2012
).
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