Simultaneous spatial-temporal image fusion using Kalman filtered compressed sensing

Pan, Han; Jing, Zhongliang; Liu, Rongli; Jin, Bo

doi:10.1117/1.oe.51.5.057005

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Another way is the application of a Gaussian low-pass filter with a kernel of 5 Â 5 pixels to the digital images before the correlation analysis. [75] The influence of fluctuations of the illumination can be encountered by the application of ZNCC or ZNSSD correlation algorithm (see Section 2.2 and point 3) this section). The used camera system can also yield "image distortions" due to lens distortion or self-heating of the digital camera.…”

Section: Sources Of Errors In 2d Dicmentioning

confidence: 99%

“…The self-heating of the camera and/or sensor can result in complex image distortions. According to Pan et al, [75] the application of bitelecentric optics, however, helps to solve the problem of self-heating. Bitelecentric optics are special optical lenses, where the entrance and exit pupil is at infinity, resulting in an orthographic view of the object.…”

Section: Sources Of Errors In 2d Dicmentioning

confidence: 99%

“…For 2D DIC, this effect can be minimized or suppressed also by the application of bitelecentric optics. [75] Otherwise, a multicamera setup can be used for 3D DIC accounting of out-of-plane displacements.…”

Section: Sources Of Errors In 2d Dicmentioning

confidence: 99%

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Review on Strain Localization Phenomena Studied by High‐Resolution Digital Image Correlation

Weidner

Biermann

2021

Adv Eng Mater

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She studied Materials Science in Freiberg and received her doctorate and habilitation. Her primary research interests focus on plasticity, fatigue, and related microstructural analysis of materials. Her particular interests are related to the application of complementary in situ characterization techniques for better understanding of kinetics of deformation and damage processes. To date she has authored or coauthored over 100 peer-reviewed publications.

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Section: Sources Of Errors In 2d Dicmentioning

confidence: 99%

Section: Sources Of Errors In 2d Dicmentioning

confidence: 99%

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Review on Strain Localization Phenomena Studied by High‐Resolution Digital Image Correlation

Weidner

Biermann

2021

Adv Eng Mater

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“…Image restoration [1][2][3][4] attempts to recover a clear image from the observations of real scenes. As a fundamental procedure, it has been applied to various application areas, such as image fusion [5] and action recognition [6]. However, typically, the noise characteristics of imaging camera is completely or partially unknown.…”

Section: Introductionmentioning

confidence: 99%

A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise

Pan¹,

Jing²,

Qiao³

et al. 2017

Applied Computational Intelligence and Soft Computing

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The removal of mixed Gaussian-impulse noise plays an important role in many areas, such as remote sensing. However, traditional methods may be unaware of promoting the degree of the sparsity adaptively after decomposing into low rank component and sparse component. In this paper, a new problem formulation with regular spectral k-support norm and regular k-support l1 norm is proposed. A unified framework is developed to capture the intrinsic sparsity structure of all two components. To address the resulting problem, an efficient minimization scheme within the framework of accelerated proximal gradient is proposed. This scheme is achieved by alternating regular k-shrinkage thresholding operator. Experimental comparison with the other state-of-the-art methods demonstrates the efficacy of the proposed method.

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“…Lately, some researchers focused on the scheme of fusing spatial-temporal information [12,13]. First, Kalman filtered compressed sensing [11] (KFCS) was presented for dealing with this problem. This method can capture spatial-temporal changes of multi-source videos by separable space-time fusion method.…”

Section: Introductionmentioning

confidence: 99%

Multi-modality Image Fusion via Generalized Riesz-wavelet Transformation

Jin¹,

Jing²,

Pan³

2014

KSII TIIS

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To preserve the spatial consistency of low-level features, generalized Riesz-wavelet transform (GRWT) is adopted for fusing multi-modality images. The proposed method can capture the directional image structure arbitrarily by exploiting a suitable parameterization fusion model and additional structural information. Its fusion patterns are controlled by a heuristic fusion model based on image phase and coherence features. It can explore and keep the structural information efficiently and consistently. A performance analysis of the proposed method applied to real-world images demonstrates that it is competitive with the state-of-art fusion methods, especially in combining structural information.

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Simultaneous spatial-temporal image fusion using Kalman filtered compressed sensing

Cited by 11 publications

References 18 publications

Review on Strain Localization Phenomena Studied by High‐Resolution Digital Image Correlation

Review on Strain Localization Phenomena Studied by High‐Resolution Digital Image Correlation

A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise

Multi-modality Image Fusion via Generalized Riesz-wavelet Transformation

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