Noise Analysis and Image Restoration for Optical Sparse Aperture Systems

Liu, Li; Jiang, Yuesong; Wang, Changwei

doi:10.1109/ettandgrs.2008.125

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…In real observation condition, images collected by CCD are inevitably affected by noises. As far as synthetic aperture imaging system is concerned, there are three main sources of noises, namely the photon noise, dark current noise and readout noise 9,10 . The photon noise is defined as the number of optoelectronics generated by background radiation in the observations field while dark current noise is the number of photoelectrons generated by CCD heating, both of which collectively refer to background noise and follow Poisson distribution; readout noise generate in the procedure of reading the signal from the sensor, which occupies a major part in the signal-weak regions and can be considered to be under the Gaussian distribution.…”

Section: Noise Influence and Accuracy Analysismentioning

confidence: 99%

A piston error detection method of optical synthetic aperture system

Wei¹,

Geng²,

Jiang³

et al. 2014

SPIE Proceedings

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The existence of phase error influences the imaging performance of optical synthetic aperture system and limits the resolution of the imaging system. The relationship between point spread function of binocular system and piston error is analyzed and the conclusion that the relation between point spread function and piston error is irrational with coordinates along the perpendicular bisector of baseline has been deduced and proved. Therefore the effect of position accuracy to error detection is avoided. The distribution characteristic of point spread function on baseline determines the range of piston error and by which a unique value of piston error is obtained. Finally, the piston error detection method of binocular system is extended to the common optical synthetic aperture system. Simulation experiment demonstrates that this method can conquer effect of various noises and the detection accuracy is better than 0.004 times of wavelength.

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Section: Noise Influence and Accuracy Analysismentioning

confidence: 99%

A piston error detection method of optical synthetic aperture system

Wei¹,

Geng²,

Jiang³

et al. 2014

SPIE Proceedings

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show abstract

“…One of the most common methods used to achieve image restoration is Wiener filter [2] [3]. However, during real imaging process, the precise estimation of transfer function and signal-to-noise ratio (SNR) cannot be obtained due to the influences of noises and phase errors, which restrains the capability of Wiener filter.…”

Section: Introductionmentioning

confidence: 99%

Multi-frame sparse aperture image restoration based on movable array

Xiao-feng

Geng

Jiang

et al. 2012

2012 International Conference on Computer Vision in Remote Sensing

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The modulation transfer function of optical sparse aperture system is attenuated among the low and middle frequency band, which causes the degradation and blur. Aimed at this problem, the Annulus array with six apertures is studied.Firstly, a half of its sub-apertures are moved towards center to respectively acquire the multi-frame images that contain integrated information. Secondly, the move scales are determined via the cut-off frequency and mid-band energy ratio. Finally, image restoration is performed by multi-frame iterative blind deconvolution algorithm. The experimental result shows that the proposed method works well on the sparse aperture image restoration of Annulus array while the reconstruction accuracy has also been validated. Moreover, the design of relative sensor can be innovated by this method.

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“…The noise affecting a given area of a homogeneous SAR image is classically supposed to be a multiplicative speckle noise with gamma or Weibull distribution [44]- [46]. Conversely, the noise affecting optical images has been considered as an additive Gaussian noise in many applications [47], [48]. The model considered in this study takes advantage of the relationships between the sensor responses to the objects contained in the observed scene, the physical properties of these objects and the statistical properties of the noise corrupting the images.…”

mentioning

confidence: 99%

A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

Prendes¹,

Chabert

Pascal

et al. 2015

IEEE Trans. on Image Process.

112

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Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.

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Noise Analysis and Image Restoration for Optical Sparse Aperture Systems

Cited by 8 publications

References 4 publications

A piston error detection method of optical synthetic aperture system

A piston error detection method of optical synthetic aperture system

Multi-frame sparse aperture image restoration based on movable array

A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

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