Compressed ghost edge imaging
Hui Guo (郭 辉)
1,2
, Ruyong He (何儒勇)
1
, Chaopeng Wei (魏朝鹏)
1
, Zequn Lin (林泽群)
1
,
Le Wang (王 乐)
1
, and Shengmei Zhao (赵生妹)
1,
*
1
Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications,
Nanjing 210003, China
2
College of Information Engineering, Fuyang Normal University, Fuyang 236037, China
*Corresponding author: zhaosm@njupt.edu.cn
Received February 21, 2019; accepted April 18, 2019; posted online July 1, 2019
In this Letter, we propose an advanced framework of ghost edge imaging, named compressed ghost edge imaging
(CGEI). In the scheme, a set of structured speckle patterns with pixel shifting illuminate on an unknown object.
The output is collected by a bucket detector without any spatial resolution. By using a compressed
sensing algorithm, we obtain horizontal and vertical edge information of the unknown object with the bucket
detector detection results and the known structured speckle patterns. The edge is finally constructed via two-
dimensional edge information. The experimental and numerical simulations results show that the proposed
scheme has a higher quality and reduces the number of measurements, in comparison with the existing edge
detection schemes based on ghost imaging.
OCIS codes: 110.1650, 110.1758.
doi: 10.3788/COL201917.071101.
Ghost imaging (GI), also called single-pixel imaging, is a
novel optical imaging technique that has received great
attention recently
[1–5]
. There are two spatially correlated
optical beams in a GI system. One beam, called the object
beam, illuminates an unknown object, and the resulting
scattered light is then collected by a bucket detector
without any spatial resolution. The other beam, named
the reference beam, never interacts with the object and
is detected by a spatially resolving detector. A ghost image
is reconstructed by correlating the bucket signal and the
reference signal, but not either one alone. Compared with
traditional imaging methods, GI can be used to recon -
struct the image of the object in various optically harsh
or noisy environments
[6]
.
Edge detection methods find edges by noticing dramatic
changes in image processing. It is extensively used in
image segmentation, target recognition, and computer
vision
[7,8]
. In traditional edge detection methods, the object
needs to be imaged first, and the edge information can be
obtained by developing a corresponding edge operator.
However, in harsh or noisy environments, the imaging step
is difficult to achieve, so the edge detection algorithm
cannot be implemented. Given the special properties of
GI, edge detection methods based on GI can solve the
problem of disturbances in the optical path and have an
advantage in edge detection. In recent years, GI-based
edge detection has achieved some results
[9–13]
. In Ref. [9],
a gradient GI (GGI) was proposed to detect edges of an
unknown object directly. However, it is a problem for
choosing a proper gradient angle based on the prior knowl-
edge of the object in this method. Subsequently, speckle-
shifting GI (SSGI) was introduced to find the edge of an
unknown object without additional prior knowledge of the
object
[10]
. Meanwhile, subpixel-SSGI was proposed, which
can enhance the resolution of edge detection with low
resolution speckle patterns
[11]
. In Ref. [12], the authors pre-
sented structured intensity patterns to find the edge of an
object directly from the data detected by computational
GI (CGI). In Ref. [
13], special sinusoidal patterns were de-
signed to find the edge of an unknown object with an im-
provement in the signal-to-noise ratio (SNR) in the
frequency domain. However, the number of measurements
of these schemes is large, and the quality of the edge de-
tection results still needs to be improved.
On the other hand, a compressed sensing (CS) method
was introduced into GI to obtain a higher resolution image
of an object by exploiting the redundancy in the structure
of the images to reduce the number of measu rements
required for exact reconstruction
[14–16]
. Therefore, a GI
method based on CS can enable the reconstruction of an
N-pixel image from much less than N measurements,
which overcomes the limitations of the Nyquist sampling
theorem and greatly reduces the acquisition time and
number of required measurements
[17,18]
.
In the Letter, we propose a GI-based edge detection
scheme that combines selected features of a CS technique.
We call our method compressed ghost edge imaging
(CGEI). In the scheme, special random patterns with char-
acteristics of different speckle-shifting are first designed. In
the CS technique, high-quality horizontal and vertical
edge information could be obtained directly from the
bucket detector detection results and the structured illu-
minations. Lastly, the global edge of the unknown object is
constructed with two-dimensional edge information.
Figure
1 shows the schematic diagram of the CGEI
scheme. The light is modulated by a digital micro-mirror
device (DMD), which is controlled by a computer to pro-
duce the speckle patterns S
k
ðx
i
; y
j
Þ, k ¼ 1; 2; …; M, where
M is the number of speckle patterns, and x
i
, y
j
are the
spatial coordinates. The bucket detector measures the
COL 17(7), 071101(2019) CHINESE OPTICS LETTERS July 2019
1671-7694/2019/071101(6) 071101-1 © 2019 Chinese Optics Letters