Atmospheric Turbulence-Degraded Image Restoration Using Principal Components Analysis

Li, Dalong; Mersereau, R.M.; Simske, Steven J.

doi:10.1109/lgrs.2007.895691

Cited by 97 publications

(67 citation statements)

References 8 publications

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“…The first image is obtained by applying the Lucky Region Fusion (LRF) method [2] to the first 100 frames of the image sequence that we have treated throughout this paper (column (i) of Figure 12), and we can see that while the geometry is reconstructed well the final result is still considerably blurry. The second image in Figure 13 is yielded by the Principal Component Analysis (PCA) technique described in [24], once again applied to the first 100 images of the same sequence: this method also reconstructs the geometry of the underlying scene correctly, and does a better job at deblurring than LRF does. However our method (albeit considerably slower than the first two) yields a result, shown on the right, that is considerably sharper.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…They perform image registration via Large Deformation Diffeomorphic Metric Mapping (LDDMM), introduced by Miller et al [4,30,46] which is computationally very expensive; moreover the final results depend heavily on the choice of the parameters of the LD-DMM registration. Li et al [24] use Principal Component Analysis (PCA) to deblur a sequence of turbulent images, simply by taking the statistically most significant vector as their estimate for the original static image; instead, Hirsch et al [21] formulate a space-variant deblurring algorithm that is seemingly computationally treatable. However neither [24] nor [21] address the issue of domain deformation and thus are only suitable when the geometric distortion is reasonably small.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [24] use Principal Component Analysis (PCA) to deblur a sequence of turbulent images, simply by taking the statistically most significant vector as their estimate for the original static image; instead, Hirsch et al [21] formulate a space-variant deblurring algorithm that is seemingly computationally treatable. However neither [24] nor [21] address the issue of domain deformation and thus are only suitable when the geometric distortion is reasonably small. Tahtali et al [41] model the image deformation process by attaching a spring-mass-damper oscillator model to each pixel with a random perturbation, and augment the state to include the unknown natural frequency and damping ratio at each location; they treat each pixel location and coordinate independently and perform Extended Kalman Filtering (EKF) to estimate the position of each pixel and produce a static image.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Linear Systems Approach to Imaging Through Turbulence

Micheli¹,

Lou

Soatto³

et al. 2013

J Math Imaging Vis

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In this paper we address the problem of recovering an image from a sequence of distorted versions of it, where the distortion is caused by what is commonly referred to as ground-level turbulence. In mathematical terms, such distortion can be described as the cumulative effect of a blurring kernel and a time-dependent deformation of the image domain. We introduce a statistical dynamic model for the generation of turbulence based on linear dynamical systems (LDS). We expand the model to include the unknown image as part of the unobserved state and apply Kalman filtering to estimate such state. This operation yields a blurry image where the blurring kernel is effectively isoplanatic. Applying blind nonlocal Total Variation (NL-TV) deconvolution yields a sharp final result.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Linear Systems Approach to Imaging Through Turbulence

Micheli¹,

Lou

Soatto³

et al. 2013

J Math Imaging Vis

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“…A long-distance imaging system can be strongly affected by atmospheric turbulence, which randomly changes the refractive index along the optical transmission path, generating geometric distortion (motion), space and timevarying blur, and sometimes even motion blur if the exposure time is not sufficiently short [1], [2], [3], [4], [5]. Aside from hardware-based adaptive optics approaches [6], several signal processing approaches have been proposed to solve this problem [7], [8], [4], [5], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Removing Atmospheric Turbulence via Space-Invariant Deconvolution

Zhu

Milanfar²

2013

IEEE Trans. Pattern Anal. Mach. Intell.

185

180

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Abstract-To correct geometric distortion and reduce space and time-varying blur, a new approach is proposed in this paper capable of restoring a single high-quality image from a given image sequence distorted by atmospheric turbulence. This approach reduces the space and time-varying deblurring problem to a shift invariant one. It first registers each frame to suppress geometric deformation through B-spline-based nonrigid registration. Next, a temporal regression process is carried out to produce an image from the registered frames, which can be viewed as being convolved with a space invariant near-diffraction-limited blur. Finally, a blind deconvolution algorithm is implemented to deblur the fused image, generating a final output. Experiments using real data illustrate that this approach can effectively alleviate blur and distortions, recover details of the scene, and significantly improve visual quality.

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“…Methods have been analyzed or presented oriented towards different applications such as interpretation of long distance surveillance data [1], reduction of atmospheric effects [2], bi-level signals [3,4] and photography [5]. More general techniques have been developed as well, varying in complexity [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Barcode-based calibration of a 1-D blur restoration pipeline

Gaubatz¹,

Simske²

2010

2010 IEEE International Conference on Image Processing

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quality assurance, blur estimation, structured printing, functional imaging, barcodesIn high-speed printed media inspection environments, image restoration pipelines play a critical role in establishing and continually evaluating performance. A key role of such systems is to understand, mitigate (and possibly remove) artifacts introduced by motion blur. An approach is proposed that uses barcodes to help calibrate a one-dimensional blur restoration pipeline. Techniques are demonstrated whereby the structure of barcode markings may be leveraged to estimate motion blur parameters, even under extreme blur conditions or when the barcode is unknown. In addition, a framework for comparing blur estimation procedures based on barcode readability is introduced. These techniques can be applied independently of one another, but together form a set of useful tools for blur restoration pipeline calibration. Within this framework, it is shown that a low-complexity blur estimation strategy demonstrates performance competitive with state-of-the-art approaches in term of speed and accuracy.Presented at IEEE International Conference on Image Processing, Hong Kong, China, September 26-29, 2010Copyright IEEE International Conference on Image Processing, 2010 BARCODE-BASED CALIBRATION OF A 1-D BLUR RESTORATION PIPELINE Matthew D. Gaubatz and Steven J. SimskeHewlett-Packard, Co. {matthew.gaubatz,steven.simske}@hp.com ABSTRACTIn high-speed printed media inspection environments, image restoration pipelines play a critical role in establishing and continually evaluating performance. A key role of such systems is to understand, mitigate (and possibly remove) artifacts introduced by motion blur. An approach is proposed that uses barcodes to help calibrate a one-dimensional blur restoration pipeline. Techniques are demonstrated whereby the structure of barcode markings may be leveraged to estimate motion blur parameters, even under extreme blur conditions or when the barcode is unknown. In addition, a framework for comparing blur estimation procedures based on barcode readability is introduced. These techniques can be applied independently of one another, but together form a set of useful tools for blur restoration pipeline calibration. Within this framework, it is shown that a low-complexity blur estimation strategy demonstrates performance competitive with state-of-the-art approaches in term of speed and accuracy.

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Atmospheric Turbulence-Degraded Image Restoration Using Principal Components Analysis

Cited by 97 publications

References 8 publications

A Linear Systems Approach to Imaging Through Turbulence

A Linear Systems Approach to Imaging Through Turbulence

Removing Atmospheric Turbulence via Space-Invariant Deconvolution

Barcode-based calibration of a 1-D blur restoration pipeline

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