close

Вход

Забыли?

вход по аккаунту

?

j.infrared.2018.08.012

код для вставкиСкачать
Infrared Physics and Technology 93 (2018) 277–285
Contents lists available at ScienceDirect
Infrared Physics & Technology
journal homepage: www.elsevier.com/locate/infrared
Regular article
Error tolerance and effects analysis of satellite vibration characteristics and
measurement error on TDICCD image restoration
T
⁎
Jianming Hua, Xiyang Zhia, , Jinnan Gonga, Zhongke Yinb, Zhipeng Fanc
a
Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China
Institute of Remote Sensing Information, Beijing 100192, China
c
University of Texas at Dallas, Dallas, TX, USA
b
A R T I C LE I N FO
A B S T R A C T
Keywords:
Satellite vibrations
Space-variant image degradation
Measurement error
Error tolerance
Satellite design
Satellite vibrations during exposure time could lead to image motion degradation, and image restoration
methods are commonly used to compensate for such degradation. However, effects of space-variant image degradation characteristics due to the vibrations for time delayed integration (TDI) camera and real-time measurement errors of the vibration motion are rarely taken into account during the restoration process, which will
bring final restored image processing error. In this paper, the analytical model of vibration motion is first deduced mathematically and then the space-variant modulation transfer function (MTF) degradation and corresponding influence factors are analyzed. Meanwhile, the effect of measurement errors is theoretically explained
on that basis of high-performance restoration model. Then the effects of the space-variant MTF and measurement
errors for different frequencies, amplitudes and sampling rate on image restoration performance are further
presented. Finally, influences of above factors affecting restoration quality are verified and the relationships
between them are established experimentally. Subsequently, the error tolerance of satellite vibration itself and
measurement specifications are quantitatively generated from the practical application point of view, which can
provide a valuable reference for future satellite platform design and the specifications determining of TDICCD
camera.
1. Introduction
Nowadays, optical remote sensing satellite images play an increasingly important role in military and civilian fields, particularly highresolution images, and new requirements are also put forward for the
interpretation accuracy of high-resolution images. However, it is difficult to avoid platform vibration during satellite imaging process completely, and for the on-board time delayed integration (TDI) camera,
satellite platform vibrations during exposure time not only cause motion blur, but also lead to irregular sampling on the image plane, resulting in geometric quality degradation [1]. Furthermore, with an increasing of image resolution, the impact of the same degree of vibration
will be significantly enlarged, which seriously affects the interpretation
precision of satellite images [2–4]. Consequently, considering that the
performance of vibration isolator or gyro-stabilized camera platform is
limited by current manufacturing technology, development cost and
cycle, image restoration methods are usually adopted to compensate for
such motion degradation.
In previous studies, lots of restoration methods have been proposed
⁎
to deal with both image deblurring [5] and geometric distortion correction [6], such as total variation regularization [7,8], processing in
wavelet domain [9], sparse representation [10]. Despite the fact that
they could be very effective in improving image quality such as modulation transfer function (MTF) improvement, noise suppression and
image details preserving [11,12], the restoration process is usually regarded as a space-invariant process, consequently, spatial variance of
the original image MTF owing to satellite vibration characteristics such
as frequency, amplitude as well as TDI Stage is rarely taken into account. Furthermore, they consider little on the effect of processing
performance due to the vibration measurement errors that determine
the accuracy of input parameters. With the improvement of image resolution, the demand for restoration quality is in urgent need, therefore
it is worth carrying on research to analyze the error tolerance of satellite vibration characteristics and measurement error for TDICCD
image restoration, providing suggestions for vibration suppression,
measurement and ground image processing.
Compared with the previous studies in the literature, the physicsbased factors related to the satellite vibrations impacting image
Corresponding author.
E-mail address: zhixiyang@hit.edu.cn (X. Zhi).
https://doi.org/10.1016/j.infrared.2018.08.012
Received 23 April 2018; Received in revised form 24 July 2018; Accepted 12 August 2018
Available online 12 August 2018
1350-4495/ © 2018 Elsevier B.V. All rights reserved.
Infrared Physics and Technology 93 (2018) 277–285
J. Hu et al.
Fig. 1. Restoration results with space-invariant and space-variant MTF: (a) original Image, (b) space-invariant MTF, (c) Degraded Image with MTF in (b), (d)
Restored Image of (c), (e) space-variant MTF, (f) Degraded Image with MTF in (e), (g) Restored Image of (f).
2. Theoretical error analysis of TDICCD image restoration caused
by satellite vibrations
restoration quality and their relationship are analyzed, and the error
tolerance with geometric distortion less than 0.1 pixel and Structural
Similarity index between the restored and ideal images over 0.9 is
discussed, which can provide a valuable and scientific reference for
satellite system design and in-orbit image applications.
The rest of this paper is organized as follows. Section 2 presents the
theoretical analysis of degradation and restoration process of high resolution TDICCD imagery, and then the image restoration processing
error caused by the platform vibration characteristics itself and the
vibration measurement error are analyzed in Section 3. In Section 4, the
maximum error tolerance of the above influence factors is studied
quantitatively. Subsequently, suggestions of satellite vibration platform
are proposed based on the semi-physical experimental results. Finally,
we come to a conclusion in Section 5.
In this Section, the models of TDICCD image degradation and restoration caused by satellite vibrations are first deduced mathematically, on this basis, two key factors having an adverse impact on the
restoration quality are discussed, which can provide a theoretical basis
for the analysis of influence factors on restoration quality in Section 3 as
well as experimental design in Section 4.
2.1. Space-variant image degradation caused by satellite vibration
characteristics
Satellite vibrations generally result from changes in-orbit attitude,
detector motion or imbalances of related components such as solar
array, antenna, etc. [13–15]. Obviously, vibrations could cause disturbance to sampling positions, which may change over time during the
imaging process. Although the measured vibration pattern may be
278
Infrared Physics and Technology 93 (2018) 277–285
J. Hu et al.
Fig. 2. Degraded image and corresponding MTF in different vibration frequencies: (a) original image, (b) fk = 0.1fes (fk is vibration frequency, fes is sample frequency), (c) Calculated MTF of (b), (d) fk = 0.35fes, (e) Calculated MTF of (d), (f) fk = 4.23fes (g) calculated MTF of (d).
279
Infrared Physics and Technology 93 (2018) 277–285
J. Hu et al.
f=400Hz
f=200Hz
f=100Hz
i+N
x¯i =
i+N
∑ ∑ x¯k,i,n = ∑ ∑
n=i
n=i
k
k
Ak fes ⎡
2π
⎤
cos(φk, i, n )−cos ⎜⎛ ·fk + φk, i, n⎟⎞ ⎥
2πfk ⎢
⎝ fes
⎠⎦
⎣
(4)
As can be seen from Eq. (4), when x̄ i ≠ 0 , it represents distortion
occurs in i-th row of acquired image. Obviously, image motion apparently affects grayscale distribution, and moreover, it leads to motion
blur in this case.
Consider that the well-studied vibration blur can be expressed as:
LSFX =
Measuring Measuring
point one point two
Measuring
point three
Measuring Measuring
point four point five
Fig. 3. Position of measuring points.
complex due to collective effects of its sources, it can usually be decomposed to the summation of several sinusoidal vibration components. And the vibration disturbance resulting from one component can
be expressed as follow:
=
1
Tes
∫0
Tes
In previous work of Yaroslavsky [16] and Zhi [17], etc, an image
restoration model of vibration-affected imaging process was proposed:
(1)
xk, i, j, n (t ) dt =
1
Tes
∫0
Tes
g = SHu + n
⎟
2π
Ak Tk ⎡
cos(φk, i, j, n )−cos ⎛ ·Tes + φk, i, j, n⎞ ⎤
2πTes ⎢
⎝ Tk
⎠⎥
⎦
⎣
⎜
⎟
(2)
̂ g||2 }
u ̂ = argmin{||u ||̂ 2 + λ||SHu −
where (i, j ) is pixel coordinate in the image plane, Tes is the single stage
integration time, Ak , Tk and φk, i, j, n denote vibration amplitude, period
and phase of k-th component respectively, among which φk, i, j, n is also
related to pixel coordinate and stage number n . Since the sensors’
movements of the same CCD stage are identical, we assume that the
effect of vibrations along j direction is constant, and omit subscript j
for simplicity. From Eq. (2), thus the image motion of n-th stage can be
derived as:
x¯i, n =
2π
A T
k k⎡
cos(φk, i, n )−cos ⎛
∑ x¯k,i,n = ∑ 2πT
⎢
T
⎜
k
k
es
⎣
⎝
k
·Tes + φk, i, n⎞ ⎤
⎠⎥
⎦
(6)
where g is the degraded image, u is the original image, S is a resampling
matrix which represents geometric distortion caused by vibrations, H is
a blurring operator, which is the inverse fourier transform of MTF, and
n is noise.
The model can describe not only image blurring induced in the
whole physics-based imaging process, but also geometric distortion
owning to irregular sampling.
Based on regularization methods [8,18], the restoration problem
can be expressed as follow:
2π
Ak sin ⎛ ·t + φk, i, j, n ⎞ dt
⎝ Tk
⎠
⎜
(5)
2.2. Effects of vibration measurement errors on image restoration quality
where A , T , and φ0 denote vibration amplitude, period and phase respectively. Consequently, for the n-th stage of TDICCD, the image
motion, which corresponds to k-th vibration component, can be described as follow:
x¯k, i, j, n =
δ (X −x (t )) dt
es
By applying numerical calculation to Eq. (5), the image MTF disturbed by vibrations can be obtained with Fourier transform.
Combined with the above formulas, it is clear that the image motion
caused by vibration in the integral time will not only cause the image
blurring, but also the position deviation of the integral pixels in each
row, which leads to space-variant MTF degradation of the TDICCD
image consequently.
Measuring
point six
2π
x (t ) = A sin ⎛ ·t + φ0⎞
⎠
⎝T
(i + N − 1) Tes
∫(i−1) T
û
(7)
where λ is the Lagrange multiplier.
Instead of solving Eq. (7) directly, it could be more convenient by
using conjugate gradient method, and then the ideal image can be
obtained by applying the inverse FFT to u .̂
In order to realize factors having an adverse impact on the restoration quality, a high-performance algorithm is then fixed based on
the theoretical methods in Eq. (7), which can implement image deblurring and geometric distortion correction simultaneously. Furthermore, the restoration solution can be obtained within seconds, which
means the algorithm can restore high-resolution images effectively with
reasonable time. Consequently, in view of the excellent efficiency and
real-time performance, analyses and experiments in the following
⎟
(3)
Summing up image motion results of all stages, assume the total
stage number is N, then the overall image motion is obtained as:
Fig. 4. Calculated results based on the actual and measured parameters: (a) MTF comparison, (b) Calculation geometric distortion comparison.
280
Infrared Physics and Technology 93 (2018) 277–285
J. Hu et al.
Fig. 5. Restoration results based on the actual parameters and measured ones, (a) restored image based on actual parameters, (b) restored image based on measured
parameters.
Fig. 6. Calculated MTF and restored images based on the actual parameters and measured ones: (a) p = 0.05 pixel, (b) restored images based on the actual
parameters with p = 0.05 pixel, (c) restored images based on the measured parameters with p = 0.05 pixel, (d) p = 0.5 pixel, (e) restored images based on the actual
parameters with p = 0.5 pixel, (f) restored images based on the measured parameters with p = 0.5 pixel.
MTF and vibration measurement error should be carried out.
Sections are based on this algorithm.
From the formula derivation in Section 2.1, it can be noticed that
considering the image MTF as a space invariant operator is not very
accurate. Indeed, it can be more serious owing to vibrations with higher
frequency and larger amplitude. Furthermore, as can be seen from the
restoration model, both calculations of S and H require high-accuracy
measurement of image motion. But limited to the current technological
level, vibration measurement error is inevitable. And when it goes beyond a certain tolerance during restoration process, serious artifacts can
be obviously realized near the nonsmooth region of the image (such as
edges and textures). Consequently, in order to ensure accuracy of restoration model input parameters, further analysis of space variance of
3. Effects of vibration characteristics and measurement errors on
image restoration quality
3.1. Effect of space-variant MTF for different frequencies and amplitudes on
restoration quality
In order to illustrate effect of the space-variant MTF on restoration
quality, a space-invariant and a space-variant MTF are added to the
same scene image respectively, and then images are restored by the
high-performance algorithm mentioned in Section 2.2. The restoration
281
Infrared Physics and Technology 93 (2018) 277–285
J. Hu et al.
Degradation Simulation
Amplitude
Frequency
TDI Stage
Integration
Time
High Resolution
WorldView-3 Images
Harbour
Airport
City
Parameter Calculation and
Image Restoration
Geometric
Distortion
Amount
PSF
Restored
Image
OTF
Data Fitting
Image
Degradation
Simulation
Degraded Image
Image Quality Assessment and
Quantitative Analysis
SSIM
Vibration Measurement Simulation
Actual Image Shift
Quantitative
Relation
Measured
Vibration
Characteristics
Measurement Error
Residual
Geometric
Distortion
Fig. 7. Experimental design.
Fig. 8. Standard scene images.
In order to illustrate space variance of the image MTF, assume the
amplitude is 1 pixel and TDI stage is 64, we take vibration frequency as
control variable. Fig. 2(b), (d) and (f) show degraded image examples
under different vibration frequencies. Moreover, as shown in Fig. 2(c),
(e) and (g), in each subimage two curves are calculated in reference
with different columns.
As shown in Fig. 2, one can easily notice that the MTF curves of two
regions show variations with the changing of vibration frequency,
which illustrates the space-variant characteristic of the imaging degradation.
Therefore, when the TDI Camera series is fixed, frequency and
amplitude are the main factors for space-variant MTF, clearly resulting
in inaccurate input for restoration process. In order to successfully
implement image restoration and have an excellent application value,
space-variance caused by vibration must be restrained, in other words,
frequency and amplitude should be controlled in a certain range.
result comparison with different MTF is shown in Fig. 1, in which (a) is
the original image, (c) and (f) illustrate degraded images while the
former has a space-invariant MTF, (d) and (g) show corresponding restored images. Through the visual effect of restored images, it can be
easily observed that restored image quality with a space-invariant MTF
is close to the original image, nevertheless, when the MTF is spacevariant, the detail distortion and edge artifacts are obvious in restored
image.
Based on existing literature [19,20], when TDI series is certain, it is
stated that the MTF calculation method for low frequency vibration can
be approximately expressed as:
ML (f ) = |J0 (πfA)|
(8)
where J0 denotes the Bessel function of the first kind.
Similarly, the MTF calculation method for high frequency vibration
is given:
Mh (f ) = |J0 (2πfA)|
(9)
282
Infrared Physics and Technology 93 (2018) 277–285
J. Hu et al.
position detecting error caused by acceleration sensor itself. Satellite
vibrations have a wide range of frequencies, while sampling rate of
vibration detector is usually fixed. If vibration frequency is higher than
sampling rate, reconstructed vibration waveform may deviate from the
actual waveform, thus affecting restoration consequence. Besides, even
the sampling rate is sufficient, because of measurement device precision
restriction, still, there may be slight variations in image pixel position.
The influence of insufficient sampling rate is analyzed below.
Assume that the speed of the vibration measurement is 1 ms per measurement point. When the number of push lines is 512, the single stage
integration time is 0.05 ms, thus the time required to complete an
image is 25 ms, and there are 25 measuring points in an image. Fig. 3
shows the positions of the measurement point with different vibration
frequencies within 5 ms. It can be seen that when the vibration frequency is 100 Hz, half of the vibration period is included within 5 ms, in
other words, there should be 11 points within the whole vibration
cycle. With an increase of frequency, for example, when it is 200 Hz, the
corresponding measuring points in vibration period will decrease.
Therefore, using a small number of measuring points is difficult to
collect the complete vibration image motion, which is bound to bring
measurement error.
Fig. 4 shows an example of reconstructed waveform with an insufficient sampling rate. It is clear that with a low sampling rate, the
reconstructed waveform deviates from the actual one, leading to an
inaccurate estimation of distortion amount. The corresponding results
based on actual and measured parameters in this case are shown in
Fig. 5, which indicate that the error owing to unsuitable sampling rate
may lead to ripple distortion at the edge of restored images.
As previously discussed, the position detecting error of measurement sensor is also an influence factor. Current detectors may suppress
the detecting error below 0.05 pixel, in addition, implementing data
fitting may alleviate the disturbance and make it relatively controllable.
However, it can be affected by working condition, resulting in degraded
restoration accuracy.
In order to specify this statement, Fig. 6 illustrates the comparison
between sensor measurement error p = 0.05 pixel and p = 0.5 pixel
when A = 1 pixel , f = 200 Hz , n = 4 , Tes = 0.05 ms, sampling rate
r = 1000 Hz. As one can easily notice from the restoration results, when
p = 0.05 pixel, the overall visual effects of restored images based on
actual and measured parameters are basically similar. However, when
p = 0.5 pixel, compared with the actual parameters result, the restored
image with measured parameters shows the unsatisfied effect such as
Fig. 9. Semi-physical experiment.
Table 1
Experimental parameter ranges.
Parameter
Range
Vibration amplitude
Vibration frequency
TDI stage
Sensor measurement error
Measurement sampling speed
0.5–2 pixels
1–1000 Hz
1–64
0.01–0.1 pixel
1 point/ms
3.2. Effect of measurement error on restoration quality
As already mentioned, vibrations not only cause motion blur, but
also the irregular change of sampling positions. In actual image processing, image restoration requires sampling positions as input for
distortion correction and MTF for deblurring. Although both factors can
be measured with proper ground targets, the obtained data cannot fully
represent the working condition of satellite because vibration is timerelevant and sometimes a random process. Therefore, measurement
error is a limiting factor on restoration applications.
Actually, measurement error in this case usually results from two
aspects, insufficient sampling rate owing to measurement speed and
Table 2
Experimental results.
Amplitude (Pixel)
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
Frequency (Hz)
100
100
100
150
150
150
200
200
200
100
100
100
150
150
150
200
200
200
TDI Stage
4
32
64
4
32
64
4
32
64
4
32
64
4
32
64
4
32
64
Assessment of degraded image
Assessment of restored image
Measurement error: 0.05 pixel
Measurement error: 0.1 pixel
SSIM
R es
SSIM
R es
SSIM
R es
0.8861
0.9017
0.9235
0.8620
0.8901
0.9212
0.8556
0.8615
0.9011
0.7655
0.8038
0.8452
0.7639
0.8018
0.8420
0.7604
0.7914
0.8411
0.6964
0.3278
0.2410
0.7038
0.4545
0.2781
0.7102
0.5902
0.3051
1.3927
0.7956
0.2919
1.4075
0.9090
0.4362
1.4203
1.1804
0.6102
0.9369
0.9514
0.9801
0.9261
0.9452
0.9792
0.9073
0.9203
0.9558
0.8289
0.8517
0.8994
0.8175
0.8451
0.8870
0.8067
0.8346
0.8813
0.0718
0.0662
0.0529
0.0939
0.0751
0.0672
0.1387
0.1287
0.1088
0.1821
0.1436
0.1246
0.1920
0.1573
0.1286
0.2214
0.1683
0.1332
0.8514
0.8744
0.8993
0.8398
0.8631
0.8837
0.7976
0.8264
0.8607
0.7089
0.7335
0.7541
0.7031
0.7275
0.7486
0.6973
0.7206
0.7369
0.1732
0.1534
0.1158
0.1953
0.1611
0.1236
0.2279
0.1732
0.1277
0.4165
0.3489
0.2689
0.4312
0.3641
0.3058
0.4437
0.3721
0.3214
283
Infrared Physics and Technology 93 (2018) 277–285
J. Hu et al.
degradation level could be seen, providing data to find out the error
tolerance of vibration parameters.
As one can easily observe that changes of amplitude, frequency, TDI
stage and measurement error all have an impact on restored image
quality. In addition, with further analysis of the assessment results and
from the practical application point of view, the following error tolerance requirements could be derived and should be considered in satellite platform design:
noise amplification and jaggy edge artifact.
From the analysis above, it is obvious that insufficient sampling rate
owing to measurement speed and sensor detecting error are the main
aspects for measurement error, which then results in inaccurate input
for restoration process. Therefore, select a suitable vibrations sampling
rate and control the sensor measurement error in a specific range are
crucial for restoration process.
4. Experimental results and discussion
(1) Measurement error has serious impact on the precision of image
restoration. Considering the current need of high-resolution satellite images, in order to make structural similarity value higher
than 0.9, the measurement error should be controlled below 0.05
pixel, meanwhile, the maximum applicable vibration amplitude for
restoration algorithm should be no more than 1 pixel.
(2) In a certain range, the increase of frequency leads to more serious
geometric distortion. From the point of restoration accuracy, to
satisfy the requirement that the residual geometric distortion is less
than 0.1 pixel, the maximum applicable vibration frequency for
restoration algorithm is 150 Hz. Therefore, vibrations above 150 Hz
should be suppressed.
4.1. Experimental design
In this Section, the effect laws proposed in Section 3 are verified by
semi-physical experiment, and the experimental design is shown in
Fig. 7. In the experiment, 50 high-resolution satellite images from
Worldview-3 are selected as standard target images, which consist of
city, airport and harbor as shown in Fig. 8. The image resolution is
0.3 m and geometry correction is applied before the experiment.
The semi-physical experiment platform is built in the laboratory,
which consists of a high speed camera, a light source, standard target
images, a piezoelectric ceramic fast deflection mirror and a vibration
console as shown in Fig. 9. The standard target image selected as real
scene is fixed, and the vibration console is connected with the piezoelectric ceramic fast deflection mirror. By adjusting the output waveform of the console, the deflection mirror can have impact on the vibration of the experimental optical path. In addition, a high-speed
camera is used to capture the target image reflected by the deflector
mirror and collect the vibration information, simulating the imaging
process of TDI.
Based on the experiment, the images are generated with different
level of vibration parameters such as frequency and amplitude.
Meanwhile, the input parameters for restoration are generated with
different level of measurement errors. In order to quantify the influence
relation and analyze the error tolerance, as shown in Table 1, experimental parameters are given a research range, respectively.
5. Conclusion
In this study, the error tolerance and effects of satellite vibration
characteristics and measurement error on TDICCD image restoration
are proposed. First, the model of space-variant image MTF containing
vibration motion of TDICCD stages is mathematically deduced, and the
vibration factors affecting the restoration performance are theoretically
proposed combined with high-performance restoration model. Second,
effects of vibration characteristics and measurement errors on image
restoration quality are proposed through the results of the theoretical
analysis. Finally, the effects of above factors affecting restoration
quality are verified and the error tolerance of vibration characteristics
and measurement is quantitatively generated by semi-physical experiment. The experimental results demonstrate that when the sampling
rate of vibration detector is fixed at 1 point/ms, in order to meet the
requirements that residual geometric distortion is less than 0.1 pixel
and SSIM value is greater than 0.9, the measurement error of vibration
detector should be below 0.05 pixel and the vibrations frequencies
higher than 150 Hz should be suppressed, meanwhile, the maximum
applicable vibration amplitude for restoration algorithm should be no
more than 1 pixel.
The analytical method can be applied to improve high-resolution
image processing performance and it possesses potential application
prospects on the satellite system design and the specifications determining of TDICCD camera in the remote sensing field.
4.2. Evaluation of restoration quality
Structural Similarity (SSIM) is a full reference image quality evaluation method [21,22], which takes luminance, contrast and structure
information of images into consideration, respectively. The contrast
information and the structure information can reflect the blur effects
and texture preserving performance efficiently. Therefore, SSIM is applied here to evaluate the performance of image restoration.
In addition, to evaluate the performance of geometric distortion
correction, homonymous points of original image and restored image
were extracted by high precision Scale-invariant feature transform algorithm (SIFT) [23,24] respectively, and a novel residual geometric
distortion metric is given by:
Conflict of interest
n
R es = ±
We declare that we have no conflicts of interest to this work.
∑i = 1 (Δx i )2 + (Δyi )2
n
(10)
References
where Δx i and Δyi denote the coordinate point difference of corresponding homonymous points from original image and restored image,
n denotes the numbers of homonymous points. Based on accurate location coordinate information of homonymous points, the R es can represent the geometric distortion precision correctly.
[1] Z.L. Wang, X.X. Zhuang, L.Q. Zhang, Effect of image motion and vibration on image
quality of TDICCD camera, Applied Mechanics and Materials, Trans Tech
Publications, 2012, pp. 584–588.
[2] C. Serief, Estimate of the effect of micro-vibration on the performance of the
Algerian satellite (Alsat-1B) imager, Optics Laser Technol. 96 (2017) 147–152.
[3] Q. Hu, Input shaping and variable structure control for simultaneous precision
positioning and vibration reduction of flexible spacecraft with saturation compensation, J. Sound Vib. 318 (1) (2008) 18–35.
[4] C. Brian, Pleiades 1B and SPOT 6 image quality status after commissioning and 1st
year in orbit, in: Proceedings of the Joint Agency Commercial Imagery Evaluation
(JACIE) Workshop, Louisville, KY, USA, 2014, pp. 26–28.
[5] W. Li, R. Chen, S. Xu, et al., Blind motion image deblurring using nonconvex higherorder total variation model, J. Electron. Image. 25 (5) (2016) 053033.
[6] M. Alghoniemy, A.H. Tewfik, Geometric distortion correction through image normalization, 2000 IEEE International Conference on Multimedia and Expo, 2000.
4.3. Relationship between restoration quality and vibration parameters and
their error tolerance
In this Section, to quantify the influence relation and analyze the
error tolerance, Table 2 lists a part of the combinations of various influence parameters and corresponding restoration image assessment
results. On this basis, the restoration performance with each
284
Infrared Physics and Technology 93 (2018) 277–285
J. Hu et al.
[16] L.P. Yaroslavsky, Compression, restoration, resampling, ‘compressive sensing’: fast
transforms in digital imaging, J. Optics 17 (7) (2015).
[17] X. Zhi, Q. Hou, X. Sun, et al., Degradation and restoration of high resolution
TDICCD imagery due to satellite vibrations, International Symposium on
Optoelectronic Technology and Application 2014: Image Processing and Pattern
Recognition, International Society for Optics and Photonics, 2014, p. 93012I.
[18] X.G. Lv, Y.Z. Song, S.X. Wang, et al., Image restoration with a high-order total
variation minimization method, Appl. Mathemat. Model. 37 (16–17) (2013)
8210–8224.
[19] A. Stern, N.S. Kopeika, Analytical method to calculate optical transfer functions for
image motion and vibrations using moments, JOSA A 14 (2) (1997) 388–396.
[20] Y. Du, Y. Ding, Y. Xu, et al., Dynamic modulation transfer function analysis of
images blurred by sinusoidal vibration, J. Optical Soc. Korea 20 (6) (2017)
762–769.
[21] Y. Ye, J. Shan, L. Bruzzone, et al., Robust registration of multimodal remote sensing
images based on structural similarity, IEEE Trans. Geosci. Remote Sens. 55 (5)
(2017) 2941–2958.
[22] L. Gomez-Chova, D. Tuia, G. Moser, et al., Multimodal classification of remote
sensing images: a review and future directions, Proc. IEEE 103 (9) (2015)
1560–1584.
[23] B. Kupfer, N.S. Netanyahu, I. Shimshoni, An efficient SIFT-based mode-seeking algorithm for sub-pixel registration of remotely sensed images, IEEE Geosci. Remote
Sens. Lett. 12 (2) (2015) 379–383.
[24] A. Sedaghat, H. Ebadi, Remote sensing image matching based on adaptive binning
SIFT descriptor, IEEE Trans. Geosci. Remote Sens. 53 (10) (2015) 5283–5293.
ICME 2000, IEEE, 2000, pp. 1291–1294.
[7] A. Makovetskii, S. Voronin, V. Kober, Total variation regularization with bounded
linear variations, Applications of Digital Image Processing XXXIX, International
Society for Optics and Photonics, 2016, p. 99712T.
[8] W. He, H. Zhang, L. Zhang, et al., Total-variation-regularized low-rank matrix
factorization for hyperspectral image restoration, IEEE Trans. Geosci. Remote Sens.
54 (1) (2016) 178–188.
[9] Y. Yang, B. Chen, Image restoration based on wavelets and curvelet, SPIE/COS
Photonics Asia, International Society for Optics and Photonics, 2014, pp.
92733I–927339.
[10] R.R. Bhawre, Y.S. Ingle, Review on image restoration using group-based sparse
representation, 2014 IEEE International Conference on Computational Intelligence
and Computing Research (ICCIC), IEEE, 2014, pp. 1–4.
[11] J. Li, Z. Liu, S. Liu, Suppressing the image smear of the vibration modulation
transfer function for remote-sensing optical cameras, Appl. Optics 56 (6) (2017)
1616–1624.
[12] D. Wang, T. Zhang, H. Kuang, Clocking smear analysis and reduction for multi
phase TDI CCD in remote sensing system, Optics Exp. 19 (6) (2011) 4868–4880.
[13] T. Sun, H. Long, B.C. Liu, et al., Application of attitude jitter detection based on
short-time asynchronous images and compensation methods for Chinese mapping
satellite-1, Optics Exp. 23 (2) (2015) 1395–1410.
[14] N. Yi-Bing, T. Yi, Z. Li-Jun, et al., Evaluation of influences of frequency and amplitude on image degradation caused by satellite vibrations, Chin. Phy. B 24 (5)
(2015) 058702.
[15] E. Azadi, S.A. Fazelzadeh, M. Azadi, Thermally induced vibrations of smart solar
panel in a low-orbit satellite, Adv. Space Res. 59 (6) (2017) 1502–1513.
285
Документ
Категория
Без категории
Просмотров
1
Размер файла
2 571 Кб
Теги
012, 2018, infrared
1/--страниц
Пожаловаться на содержимое документа