Strip non-uniformity correction in uncooled long-wave infrared focal plane array based on noise source characterization

Cao, Yanan; Li, Yiqun

doi:10.1016/j.optcom.2014.10.041

Cited by 23 publications

(11 citation statements)

References 20 publications

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“…According to [12], FPN has a vertical and periodic property and they demonstrated that FPN is densely extracted in the horizontal coefficients of the Haar discrete wavelet transform (HDWT). To the best of our knowledge, Cao et al [15] derived the relationship between FPN and infrared data within a column through their thermal calibration experiments for the first time. From results of experimental analysis, they exploited a non-linear cubic FPN model to be used their training data [4].…”

Section: A Fpn Propertiesmentioning

confidence: 99%

“…In order to examine the FPN properties against the FPA temperature variations, we have conducted the experiments to diagnose real infrared images referring to [15]. First, we adjusted the FPA temperature from 25 to 50 degrees Celsius while the blackbody temperature fixed (i.e., incident energy from the scene is constant), and obtained the average image of 1000 frames at each FPA temperature to exclude temporal random noise.…”

Section: A Parametric Fpn Modelmentioning

confidence: 99%

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Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model

Lee

2020

IEEE Access

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The stripe fixed pattern noise (FPN) of infrared images significantly corrupts image quality, so that most infrared imaging systems suffer from the degradation of visibility and detectability during operation. Therefore, the FPN de-striping method, which eliminates stripe patterns without substantial loss of image information, remains a core technology in the field of infrared image processing. In this paper, we propose the dual-branch structure based FPN de-striping deep convolutional neural network (DBS-DCN) to effectively extract structural features of FPN and preserve the image details in a single infrared image. In addition, we have established the parametric FPN model through the diagnostic experiments of infrared images based on the physical principle of an infrared detector and its signal response. We have optimized each parameter of the FPN model using measured data, which acquired on a wide range of detector temperatures. Further, we generate the training data using our FPN model to ensure stable learning performance against various stripe patterns. We performed comparative experiments with state-of-the-art methods using artificially corrupted infrared images and real corrupted infrared data, and our proposed method achieved outstanding de-striping results in both qualitative and quantitative evaluation compared to existing methods.

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Section: A Fpn Propertiesmentioning

confidence: 99%

Section: A Parametric Fpn Modelmentioning

confidence: 99%

Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model

Lee

2020

IEEE Access

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“…The high frequency component F(i) includes stripe noise N(i) and vertical texture information V(i) [38]. The high-frequency component F(i) is decomposed by wavelet and can be expressed by the formula:…”

Section: Wavelet Transform Image Multi-scale Decompositionmentioning

confidence: 99%

Infrared stripe correction algorithm based on wavelet decomposition and total variation-guided filtering

Wang

Jiang

et al. 2019

J. Eur. Opt. Soc.-Rapid Publ.

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Stripe non-uniformity severely affects the quality of infrared images. It is challenging to remove stripe noise in lowtexture images without blurring the details. We propose a single-frame image stripe correction algorithm that removes infrared noise while preserving image details. Firstly, wavelet transform is used for multi-scale analysis of the image. At the same time, Total variation model is used for small window to smooth the original image. The small-scale total variation model can well preserve the edge information of the image, but it will leave stripe noise. Therefore, according to the prior knowledge of the vertical component of the stripe noise, the spatial filtering is finally performed: the smoothed image is used as the guide image for the stripe noise denoising. It is possible to prevent the lead filter from mistaking the strong stripe noise as edge detail, resulting in corrected image residual streak noise. The algorithm is systematically evaluated by experiments on simulated images and original infrared images, as well as compared with the current advanced infrared stripe non-uniformity correction algorithms. It is proved that our algorithm can better eliminate stripe noise and preserve edge details.

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“…For instance, Tendero et al [ 7 ] assumed that detectors in adjacent columns observe the same range of data implying that their histograms should be nearly equal, otherwise they are mapped to match a fixed reference histogram. Cao et al [ 6 , 8 ] studied the relationship between the stripe noise and the scene data and derived a polynomial model that they used to distinguish between edges and textures that belong to the scene and the actual stripe FPN. Chang et al [ 9 ] proposed to treat the destriping problem as a decomposition task using both the low-rank constraint, that exploits characteristics of the stripe noise, and the spectral information of the remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Edge-Aware Unidirectional Total Variation Model for Stripe Non-Uniformity Correction

Boutemedjet

Deng

Zhao

2018

Sensors

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The problem of stripe non-uniformity in array-based infrared imaging systems has been the focus of many research studies. Among the proposed correction techniques, total variation models have been proven to significantly reduce the effect of this type of noise on the captured image. However, they also cause the loss of some image details and textures due to over-smoothing effect. In this paper, a correction scheme is proposed based on unidirectional variation model to exploit the direction characteristic of the stripe noise, in which an edge-aware weighting is incorporated to convey image structure retaining ability to the overall algorithm. Moreover, a statistical-based regularization is also introduced to further enhance correction performance around strong edges. The proposed approach is thoroughly scrutinized and compared to the state-of-the-art de-striping techniques using real stripe non-uniform images. Results demonstrate a significant improvement in edge preservation with better correction performance.

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Strip non-uniformity correction in uncooled long-wave infrared focal plane array based on noise source characterization

Cited by 23 publications

References 20 publications

Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model

Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model

Infrared stripe correction algorithm based on wavelet decomposition and total variation-guided filtering

Edge-Aware Unidirectional Total Variation Model for Stripe Non-Uniformity Correction

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