ARL-TR-8281 ● JAN 2018

US Army Research Laboratory

Designing for Compressive Sensing: 

Compressive Art, Camouflage, Fonts, and 

Quick Response Codes

by Michael L Don

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ARL-TR-8281 ● JAN 2018

US Army Research Laboratory

Designing for Compressive Sensing: 

Compressive Art, Camouflage, Fonts, and 

Quick Response Codes

by Michael L Don

Weapons and Materials Research Directorate, ARL

Approved for public release; distribution is unlimited.
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Designing for Compressive Sensing: Compressive Art, Camouflage, Fonts, and 

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14. ABSTRACT

Compressive sensing is a signal processing technique that takes advantage of a signal's sparsity to reduce sampling requirements. 

This can be used to improve system resolution, frame rate, power consumption, and memory usage. Most compressive sensing 

research has focused on developing sensing systems and demonstrating their performance sensing preexisting objects. Little 

research has been directed toward designing the objects being sensed. This report addresses this overlooked area. Simple 

examples are shown demonstrating the advantage of modifying an object’s sparsity to increase or decrease compressive sensing 

performance. This leads to more complex object recognition examples where the object's sparsity must be balanced against its 

quality for optimal performance. In these cases, simulation results show that there are significant gains when one designs for 

compressive sensing.

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compressive sensing, fonts, optical character recognition, quick response codes, camouflage, compression

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Contents

List of Figures iv

1. Introduction 1

2. Compressive Sensing Theory 2

3. Designing for Sparsity 9

4. Designing for Non-Sparsity 11

5. Designing for Recognition 12

6. Conclusion 19

7. References 20

List of Acronyms 22

Distribution List 23
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iv

List of Figures

Fig. 1 Moire pattern example .......................................................................... 2

Fig. 2 MICR font............................................................................................. 2

Fig. 3 Simple CS example............................................................................... 4

Fig. 4 Single-sided amplitude spectrum of signal in Fig. 3............................. 4

Fig. 5 CS example using the DCT as the sparse basis .................................... 5

Fig. 6 Single-pixel camera diagram ................................................................ 6

Fig. 7 Two example images, the cameraman and the letter “R”..................... 8

Fig. 8 The first 10,000 wavelet coefficients in descending order (top) and the 

CS reconstructed PSNR vs. compression ratio (bottom) of the images 

in Fig. 7 ................................................................................................. 8

Fig. 9 Examples of CS imaging in non-sparse (top) and sparse (bottom) 

environments....................................................................................... 10

Fig. 10 Comparison of the wavelet coefficients of the 2 images in Fig. 9...... 10

Fig. 11 Example of normal image compression for a non-sparse (top) and 

sparse (bottom) image......................................................................... 11

Fig. 12 Compressive camouflage example ..................................................... 12

Fig. 13 Comparison of the wavelet coefficients of the 2 images in Fig. 12.... 12

Fig. 14 CS OCR example using a standard font ............................................. 13

Fig. 15 CS OCR example using a compressive font....................................... 14

Fig. 16 PSNR of the recovered OCR image over a range of sparsity and 

compression ratios .............................................................................. 15

Fig. 17 OCR results from simulations in Fig. 16. Yellow indicates the 

recovered text was correctly recognized............................................. 15

Fig. 18 An OCR example using normal image compression.......................... 16

Fig. 19 CS non-sparse QR example ................................................................ 17

Fig. 20 CS sparse QR example........................................................................ 17

Fig. 21 PSNR of the recovered QR code over a range of sparsity and 

compression ratios .............................................................................. 18

Fig. 22 QR code decoding results from simulations in Fig. 21. Yellow 

indicates the recovered text was correctly decoded............................ 18

Fig. 23 Comparison of the wavelet coefficients of the OCR and QR code 

example images................................................................................... 19
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1

1. Introduction

Compressive sensing (CS) is a relatively new field that has caused a lot of 

excitement in the signal processing community. It has superseded Shannon’s timehonored sampling theorem, which states that the sampling rate of a signal must be 

at least twice its highest frequency.1In CS, the necessary sampling rate depends on 

the sparsity of signal, not its highest frequency, reducing sampling requirements for 

many signals that exhibit natural sparsity.2,3 This compression happens on the 

hardware level, allowing systems to be designed with benefits ranging from 

increased resolution and frame rates to decreased power consumption and memory 

usage.4 Despite this enthusiasm for CS and the large quantity of research being 

performed, the number of commercial systems that use CS is relatively few. The 

problem of designing a CS strategy that increases functionality while actually 

reducing overall system cost has not been solved in many areas.5 This is a 

developing field where not only are new applications for CS still being developed 

but also fundamental aspects of CS theory are still evolving.

Even though CS has not become ubiquitous at this early date, one can look forward 

to a time in which it plays an important role in many sensing systems. Considering 

this possible future, it is important not only to properly design the CS sensor, but to 

also consider how the objects being sensed can be designed to increase overall 

system performance. This idea is not unique to CS; examples of designing objects 

to improve the performance of specific technologies can be found in other areas as 

well. The image on the left of Fig. 1 shows a moire pattern caused by interference 

between the shirt’s stripes and the pattern of the imaging array.6 When television 

(TV) newscasters are told to avoid clothes that could cause these patterns,7the 

objects being sensed (the newscasters) are effectively being designed to increase 

the performance of the sensing system (the TV cameras). Another example is the 

magnetic ink character recognition (MICR) font shown in Fig. 2. This font is used 

on checks and was designed not only to be readable by humans but also to increase 

the character recognition performance of MICR readers.8
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Fig. 1 Moire pattern example

Fig. 2 MICR font

Before exploring how objects can be designed for CS, a short review of CS theory 

is presented. Next, simple examples are shown demonstrating the advantage of 

modifying an object’s sparsity to increase or decrease CS performance. This leads 

to more complex object recognition applications where an object’s sparsity must be 

balanced against other factors. Increasing an object’s sparsity improves CS

performance, resulting in higher reconstruction quality and improved object 

recognition. But the very act of increasing sparsity distorts the object, which can 

impair recognition. Simulation results show that by balancing these competing 

factors an optimal design can be achieved.

2. Compressive Sensing Theory

In traditional sampling, the value of the signal is digitized at specific points in time 

and space. In CS, a sample is formed by taking the sum of the signal projected onto 

a random code. More formally, given a signal f ∈ RN and a random code h ∈ RN, 

a compressive sample g is

g = h

Tf. (1)

Each compressive sample uses a different random code. For M measurements, these 

codes become the rows of a sensing matrix H ∈ RM×N to produce the measurement 

vector g ∈ RM given by
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g = Hf. (2)

CS requires f to be sparse, but not necessarily in the standard basis; it can be sparse 

in any basis. Given the sparse coefficients θ of f in basis Ψ, the measurements can 

be rewritten as

g = HΨθ = Aθ, (3)

where f = Ψθ and the system matrix A is defined as A = HΨ. Given that N > M, 

there are an infinite number of solutions to Eq. 3, but CS theory states that if there 

are a sufficient number of measurements, the sparsest solution will recover the 

original signal. This can be found through L1-norm minimization denoted as

min

θ

‖θ‖1 subject to g = Aθ.

(4)

The number of measurements required to reconstruct f is

M ≥ C ∙ μ

2

∙ ‖θ‖0 ∙ log N,

(5)

where C is a positive constant, ‖θ‖0 is the number of nonzero values in θ, and μ is 

a small constant determined by the structure of A called mutual coherence. The 

important point is that the number of measurements is linearly related to its sparsity 

‖θ‖0, but only logarithmically related to the signal’s size, allowing large sparse 

signals to be sampled with relatively few measurements.

Figure 3 shows a simple CS example. A sparse signal f of length N = 64 is shown 

at the top. There are 4 nonzero values (i.e., ‖θ‖0 = 4). The next plot shows the 

random code h used for the first measurement produced from a zero mean, unit 

variance Gaussian random variable. A compression ratio of 0.25 is used, giving a 

total of M = N/4 = 16 measurements. The random code for each measurement 

appears as a row in the sensing matrix H ∈ R16×64. After the measurement vector 

g is determined by Eq. 2, L1-norm minimization is used to recover the original 

signal. The recovered signal is almost identical to the original, with the error shown 

in the bottom plot. Figure 4 shows the spectrum of this signal, assuming a sampling 

frequency of 1 Hz. Since the signal has frequency components up to 0.5 Hz, 

Shannon’s sampling theory would predict that the sampling rate must be twice this 

frequency to recover the signal (i.e., all 64 samples are necessary to recover the 

original signal). In this example, CS was able to recover the signal using only 25% 

of this number of measurements.
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Fig. 3 Simple CS example

Fig. 4 Single-sided amplitude spectrum of signal in Fig. 3

In the previous example, the signal was sparse in the standard basis and therefore 

it was not necessary to change the basis during CS recovery. Figure 5 shows an 

example where the signal is non-sparse in the standard basis, but sparse in the 

discrete cosine basis Ψ. The top plot shows the signal from the previous example, 

now used as sparse discrete cosine transform (DCT) coefficients θ. The next plot 

shows the non-sparse signal f in the standard basis, after applying an inverse DCT 

on the DCT coefficients. L1-norm minimization is used to recover the DCT, which 
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can then be transformed into the standard basis. Once again the reconstruction is 

almost identical, with a small error shown on the bottom plot. The rest of this report 

uses, unless otherwise noted, a binary block diagonal sensing matrix,9a wavelet 

basis, and the gradient projection for sparse reconstruction10 recovery algorithm.

Fig. 5 CS example using the DCT as the sparse basis

Although CS is a powerful sensing technique, it does have disadvantages compared 

to traditional sampling. The L1-norm minimization algorithms used for recovery 

are computationally intensive and challenging to implement in real-time systems, 

whereas in traditional sampling the data is immediately available. In addition, the 

cost and complexity of the hardware necessary to produce the compressive samples 

frequently outweighs the benefits of CS. Many researchers are optimistic that these 

problems will be solved, allowing CS to become widespread in many sensor 

systems. Currently, however, there are relatively few examples of commercially 

successful CS hardware. This presents another benefit of designing for CS. By 

lowering expectations and concentrating on environments that have been designed 

to provide high CS performance, practical applications may be found for CS that 

had once been thought to be unrealistic.

In order to illustrate an example of CS, see its advantages and disadvantages, and 

gain a broader understanding of the benefits of designing for CS, we look at an 

early implementation of a CS imager, Rice University’s single-pixel camera.11 A 

system block diagram is shown in Fig. 6. The scene is focused on a digital 
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micromirror device (DMD), which has a matrix of small mirrors that can be 

individually programmed to point toward or away from the detector. This is used 

to impose a pseudorandom block-unblock mask on the scene, which is represented 

by the binary code in the figure. All of the light directed toward the single detector 

is summed and digitized to create a single CS sample. The random pattern of the 

DMD is changed for each measurement to create a number of compressive samples. 

The diagram shows the CS samples being wirelessly transmitted before the image 

is recovered.

Fig. 6 Single-pixel camera diagram

This CS camera has a number of advantages:

 The resolution of the imager is increased from a single pixel to the number 

of mirrors of the DMD, which is available at resolutions up to 3840 × 

2160.12

 There are fewer measurements taken, which can reduce power consumption 

and increase the frame rate when compared to raster scanning.

 The samples are already compressed when acquired, reducing memory and 

bandwidth requirements.

 For each sample, the single detector receives more light than a highresolution detector, which can reduce noise in some applications. Singlepixel sensors do not experience the pixel-bleeding problems of larger 

infrared (IR) imaging arrays and can therefore use inexpensive cooling 

systems for IR applications.13

A/D

Image

Recovery

TX

RX

DMD

Scene

Single Pixel 

Detector

Imaging 

Lens

Objective 

Lens

Recovered 

Image
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Although these advantages sound impressive, this CS imager might not be practical 

for many applications. The benefits of CS must be carefully weighed against the 

following disadvantages:

 The DMD itself is an expensive instrument that increases the cost and 

complexity of the system.

 Computationally intensive algorithms are needed to recover the image.

 Although CS increases the frame rate when compared to other scanning 

technologies, it is still much slower than single-snapshot imaging arrays.

 Like other scanning technologies, the slow acquisition rate makes it 

unsuitable for many mobile applications.

 The DMD can only be used for wavelengths from 300 to 2700 nm.14 This 

excludes some of the most costly sensors that would have benefited the most 

from CS.

By examining these advantages and disadvantages, a viable design for this singlepixel camera begins to emerge. This imager is well suited for IR applications where 

a single-pixel sensor can provide significant cost savings over high-cost IR imaging 

arrays. Ideally, the target application should be static, have low frame rate 

requirements, and only require the images for postprocessing. Low-light 

environments can take advantage of the increased light seen by the detector. 

Underlying many of these considerations is the basic CS performance, evaluated 

by the number of measurements needed to obtain the desired image quality. Greater 

CS performance can translate into higher-resolution images, faster frame rates, and 

reduced power, making it an essential parameter for practical CS hardware design. 

A camera design whose disadvantages outweigh its advantages in general 

environments may become more practical if its CS performance is improved by 

limiting it to sparse environments. This idea is already applied by many researchers 

who use artificially sparse data sets to enhance the performance of their 

compressive sensors. Technical papers have been known to use simple shapes,15

Lego men,16 or—in the case of Rice’s CS camera—a single letter to demonstrate 

CS performance.

Figure 7 shows an example of the dramatic difference in CS performance between 

using a carefully selected sparse image and a typical image. On the left side of 

Fig. 7 is the classic cameraman image and on the right side is an “R” similar to an 

example image used by the single-pixel camera. The top plot of Fig. 8 shows the 

first 10,000 wavelet coefficients of these images in descending order. The sharper 

decline of the “R” coefficients compared to the cameraman coefficients indicate the 

greater sparsity of the “R” image. This translates to the greater CS performance 
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shown in the lower plot, with the “R” image reconstruction consistently exhibiting 

a higher peak signal-to-noise ratio (PSNR) than the cameraman image over a range 

of compression ratios. As in the original single-pixel camera article, a total variation 

reconstruction algorithm was used here that is similar to L1 minimization.17

Fig. 7 Two example images, the cameraman and the letter “R”

Fig. 8 The first 10,000 wavelet coefficients in descending order (top) and the CS 

reconstructed PSNR vs. compression ratio (bottom) of the images in Fig. 7

Researchers use sparse example images to simplify problems and advance their 

research. Instead of just using sparse data as a stepping stone to more challenging 

environments, this report considers designing for CS an area of research in its own 

right.
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3. Designing for Sparsity

In the introduction, we used newscasters avoiding clothing that can cause moire 

patterns as an example of designing the objects being sensed to increase the 

performance of the sensing system. In a similar vein, given that CS performance is 

proportional to sparsity, it follows that scenes can be designed with greater sparsity 

to increase the performance of CS cameras. Let us look at an example.

The 44th President of the United States Barack Obama18 is addressing the nation 

framed by artwork in the background. Since abstract expressionism was the first 

truly American art that transformed the US into the center of the art world,19 an 

abstract expressionist painting is chosen for this important event. If the video 

camera uses CS with a compression ratio of 0.35, should a Jackson Pollock or Mark 

Rothko painting be chosen? The upper 2 images in Fig. 9 show the original 

uncompressed and CS-recovered images using the Jackson Pollock painting 

“Number 1” for the background.20 Figure 10 shows a comparison of the Pollock 

image’s wavelet coefficients, vectorized and sorted in descending order, with those 

of the Rothko image. Observe that Jackson Pollock paintings are decidedly not 

sparse, containing many more significant wavelet coefficients than the Mark 

Rothko painting. This results in a disappointing PSNR of 20 dB in the recovered 

Pollock image in the rectangle containing the president. The lower 2 images using 

Mark Rothko’s much sparser “Orange and Yellow” canvas21 give a more acceptable 

PSNR of 29 dB for the president's face. Clearly, in this scenario, designing the 

environment for sparsity greatly enhanced image quality. 
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Fig. 9 Examples of CS imaging in non-sparse (top) and sparse (bottom) environments

Fig. 10 Comparison of the wavelet coefficients of the 2 images in Fig. 9

This simple example is not unique to compressive sensing; any normal image 

compression produces similar results. Image compression can be accomplished by 

retaining the amplitude and location of only the largest coefficients in a sparse 

basis. Figure 11 shows an example of compressing the images from Fig. 9 by 

retaining the largest 10% of the wavelet coefficients. Since the Pollock image is not 

sparse, this process removes a significant amount of information, creating the 

blurred image in the upper right. The Rothko image is sparse, therefore little 

information is lost during the compression process resulting in the high-quality 
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image in the lower right. Although the examples here and in the next section also 

apply to normal compression, we discuss applications in Section 5 that are unique 

to CS.

Fig. 11 Example of normal image compression for a non-sparse (top) and sparse (bottom) 

image

4. Designing for Non-Sparsity

In general, CS applications seek to increase image quality, but applications can be 

envisioned where we desire to decrease CS performance. Imagine a futuristic ninja 

battling CS-equipped unmanned aerial vehicles. What clothing would be more 

effective in confusing the CS sensors of these drones, the black outfit of the 

traditional dramatized ninja22 or a CS camouflage consisting of a random noise 

pattern? Using a compression ratio of 0.35, the black outfit in the top of Fig. 12

does not provide much of a disguise, resulting in a PSNR of 35 dB for the rectangle 

containing the ninja’s eyes. In the bottom images, the non-sparsity of the noise acts 

as a CS camouflage, hiding the eyes with a PSNR of 16 dB. Figure 13 shows a 

comparison of the black ninja’s wavelet coefficients, vectorized and sorted in 

descending order, with those of the CS camouflage image showing the dramatic 

decrease in sparsity possible when one designs for non-sparsity.
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Fig. 12 Compressive camouflage example

Fig. 13 Comparison of the wavelet coefficients of the 2 images in Fig. 12

5. Designing for Recognition

In the previous examples the relationship between sparsity and CS performance 

was straightforward: the sparser the image, the greater the CS performance. In 

Section 3, this relationship was used to increase the quality of the recovered image, 

while in Section 4, it was purposely used to obscure the image. Some applications 

exist, however, where image sparsity must be balanced against other factors. One 

example is using a compressive imager to perform optical character recognition 
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(OCR). Sparse compressive fonts can be designed to improve performance when 

imaged by a CS camera. If we make the fonts too sparse, however, it might degrade 

the quality of the fonts to the extent that they will be unrecognizable. Does it make 

sense to try to design fonts for CS imaging to improve OCR? Intuition suggests yes, 

since typically the sparsity of an image can be decreased considerably without 

significantly affecting image quality, while problems with CS recovery due to nonsparsity can create artifacts in the image that may hamper OCR.

Figure 14 shows example text in an unmodified Ariel Black bold font in the upper 

left. A CS-recovered version using the DCT and a compression ratio of 0.2 is shown 

on the lower left, with thresholding applied in the lower right. This is the maximum 

compression at which an OCR algorithm23 was able to correctly identify the text in 

the thresholded image. Figure 15 shows a modified font in the upper right created 

using only the largest 3% of DCT coefficients. Here the text was able to be 

successfully identified when only using a compression ratio of 0.15. The fact that 

the OCR algorithm failed to recognize the original font using a compression ratio 

of 0.15, but successfully recognized the modified font, shows that reducing the 

font’s sparsity increased OCR performance.

Fig. 14 CS OCR example using a standard font
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Fig. 15 CS OCR example using a compressive font

In order to analyze the relationship between the font’s sparsity and its CS 

performance, the example in Fig. 15 was repeated over a range of sparsity values 

and compression ratios. In this context, sparsity refers to the percent of DCT 

coefficients from the standard text image used to create the sparse image. Figure 16

shows the resulting PSNR of the recovered text images for this experiment. 

Figure 17 indicates successful character recognition in yellow. As expected, there 

is a balance between sparsity and image quality that yields optimal results. 

Moreover, this balance in not identical for all compression ratios. A sparsity of 3% 

allows correct text identification using only a compression ratio of 0.15. A sparsity 

of 2.5% works for a compression ratio of 0.175, but not for 0.15. Since image 

sparsity should improve recovery, it is safe to assume that in this case the 

degradation of the image due to the sparsity is combining with the effects of CS 

recovery to prevent correct character identification. Thus the sparsity, CS 

reconstruction error, and image quality all have to be balanced in order to achieve 

optimal performance.
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Fig. 16 PSNR of the recovered OCR image over a range of sparsity and compression ratios

Fig. 17 OCR results from simulations in Fig. 16. Yellow indicates the recovered text was 

correctly recognized.

In Section 3, we noted that designing for sparsity applies to normal image 

compression as well as CS. The same is true for the CS camouflage example in 

Section 4. However, the examples in this section of designing for recognition do 

not apply to normal image compression. The final image will appear the same 

whether the text is imaged and then compressed, or if it is first compressed and then 

imaged. Increasing the sparsity of the physical text serves no purpose. If the text

image is compressed, the only factor that must be considered is the maximum 

amount of compression before OCR errors begin to occur. The upper right of 

Fig. 18 shows the text compressed by retaining 0.9% of the DCT coefficients. This 
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16

was the maximum compression before OCR errors occurred. This is a less 

interesting scenario than designing fonts for CS where both the font’s sparsity and 

CS recovery error must be taken into account to optimize font design.

Fig. 18 An OCR example using normal image compression

An experiment similar to the compressive font experiment was conducted on QR 

codes with similar results. Figure 19 shows an original, non-sparse image of a QR 

code in the upper left encoding the text, “This is a test”.

24 A CS compression ratio 

of 0.44 allowed for correct decoding of the message in the recovered image shown 

in the upper right. Figure 20 shows a QR code using only the largest 5% of wavelet 

coefficients. Here the compression ratio threshold for successful recognition was 

0.1. Once again, increasing the image’s sparsity increases recognition performance.
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Fig. 19 CS non-sparse QR example

Fig. 20 CS sparse QR example
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Figure 21 shows the resulting PSNR of the recovered QR codes over a range of 

sparsity values and compression ratios. Figure 22 indicates the successful decoding 

of the simulations from Fig. 21 in yellow. Here a sparsity of 5% resulted in the best 

CS performance, allowing correct decoding using a compression ratio of 0.1. 

Comparing Fig. 22 to the analogous OCR results in Fig. 17, the CS performance 

deteriorates much more quickly for QR codes than for OCR as the images become 

less sparse. This is probably due to the QR code design that has many small features 

and hence is naturally less sparse than the OCR example, as shown in Fig. 23.

Fig. 21 PSNR of the recovered QR code over a range of sparsity and compression ratios

Fig. 22 QR code decoding results from simulations in Fig. 21. Yellow indicates the recovered 

text was correctly decoded.
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Fig. 23 Comparison of the wavelet coefficients of the OCR and QR code example images

6. Conclusion

This report has explored the concept of designing for CS. Although at this time CS 

has not become commercially widespread, many of the sensing systems of the 

future may incorporate CS into their design. If so, there will be instances where it 

will be advantageous to design the objects being sensed for CS in addition to 

designing the sensing systems themselves. Simple examples were shown 

demonstrating the advantage of modifying an object’s sparsity to increase or 

decrease CS performance. This led to more complex object recognition examples 

where the object’s sparsity was balanced against its quality to achieve optimal 

performance. In these cases, simulation results showed that there were significant 

gains when one designs for CS.
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7. References

1. Shannon CE. Communication in the presence of noise. Proc IRE.

1949;37(1):10–21. 

2. Candès EJ, Romberg JK, Tao T. Stable signal recovery from incomplete and 

inaccurate measurements. Commun Pure Appl Math. 2006;59(8):1207–1223.

3. Donoho DL. Compressed sensing. IEEE Trans Inf Theory. 2006;52(4):1289–

1306.

4. Candès EJ, Wakin MB. An introduction to compressive sampling. IEEE Signal 

Process Mag. 2008;25(2):21–30.

5. Strohmer T. Measure what should be measured: progress and challenges in 

compressive sensing. IEEE Signal Process Lett. 2012;19(12):887–893.

6. Wikimedia Commons. File:Shirt with moiré caused by aliasing.jpg. 2015 Mar 

23 [accessed 2017 Oct 19]. https://commons.wikimedia.org/wiki/

File:Shirt_with_moir%C3%A9_caused_by_aliasing.jpg.

7. PLSN. A word About Moiré [accessed 2017 Oct 19]. http://plsn.com/currentissue/35-video-world/12215-a-word-about-moire.html.

8. Wikipedia. Magnetic ink character recognition. 2017 Dec 1 [accessed 2017 Oct 

19]. https://en.wikipedia.org/wiki/Magnetic_ink_character_recognition.

9. Do TT, Tran TD, Gan L. Fast compressive sampling with structurally random 

matrices. Proceedings of 2008 IEEE International Conference on Acoustics, 

Speech and Signal Processing; 2008 Mar 31–Apr 4; Las Vegas, NV. IEEE; 

2008.

10. Nowak RD, Wright SJ, Figueiredo MA. Gradient projection for sparse 

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IEEE J Sel Top Signal Process. 2007;1(4):586–597.

11. Duarte MF, Davenport MA, Takhar D, Laska JN, Sun T, Kelly KE, Baraniuk 

RG. Single-pixel imaging via compressive sampling. IEEE Signal Process

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12. Texas instruments DLP660TE chipset [accessed 2017 Oct 20]. 

https://www.ti.com/product/DLP660TE.

13. Team single pixel camera. c2007 [accessed 2017 Oct 20]. 

http://elec424.rice.edu/spc/.
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14. Zhang K, Huang Y, Yan J, Sun L. Dynamic infrared scene simulation using 

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Apr;26(2):394–400.

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programming [accessed 2018 Jan 12]. 

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https://www.mathworks.com/matlabcentral/fileexchange/29239-qr-codeencode-and-decode.
Approved for public release; distribution is unlimited.

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List of Acronyms

ARL US Army Research Laboratory

CS compressive sensing

DCT discrete cosine transform

DMD digital micromirror device

IR infrared

OCR optical character recognition

PSNR peak signal-to-noise ratio

QR quick response

TV television
Approved for public release; distribution is unlimited.

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1 DEFENSE TECHNICAL

(PDF) INFORMATION CTR

DTIC OCA

2 DIR ARL

(PDF) IMAL HRA 

RECORDS MGMT

RDRL DCL

TECH LIB

1 GOVT PRINTG OFC

(PDF) A MALHOTRA

26 ARL

(PDF) RDRL WML E

F FRESCONI

RDRL WML F

B ALLIK 

B ACKER 

T BROWN 

S BUGGS 

E BUKOWSKI 

J COLLINS 

J CONDON 

B DAVIS 

M DON 

D EVERSON 

D GRZYBOWSKI 

R HALL 

J HALLAMEYER 

M HAMAOUI 

T HARKINS 

M ILG 

B KLINE 

J MALEY 

C MILLER 

P MULLER 

B NELSON 

D PETRICK 

K PUGH 

N SCHOMER 

B TOPPER
Approved for public release; distribution is unlimited.

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