Single image stripe nonuniformity correction with gradient-constrained optimization model for infrared focal plane arrays

Zhao, Jincheng; Zhou, Qiang; Chen, Yueting; Liu, Tao; Feng, Hao; Xu, Zhihai; Li, Qi

doi:10.1016/j.optcom.2013.01.038

Cited by 37 publications

(19 citation statements)

References 14 publications

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“…within a column) and apply a linear correction model to normalize these pixels so that adjacent channels produce output with the same mean and standard deviation. In [17], the authors proposed a single frame scene-based NUC algorithm using gradient-based regularization. The solution aims to seek the optimal image with a vertical gradient as close to the original image as possible and make the energy of the horizontal gradient as small as possible.…”

Section: Related Workmentioning

confidence: 99%

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

Cao¹,

Li²

2015

Optics Communications

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Section: Related Workmentioning

confidence: 99%

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

Cao¹,

Li²

2015

Optics Communications

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“…Compared with other excellent NUC algorithms [14][15][16][17], the proposed algorithm has two advantages. First, it does not need to deal with multiple frames of images to cause unnecessary fuzzy problem.…”

Section: Introductionmentioning

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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“…Therefore, removing the FPN in a single frame by a priori Symmetry 2018, 10, 612; doi:10.3390/sym10110612 www.mdpi.com/journal/symmetry information is currently the main research direction. Recent state-of-the-art methods for single frames include midway histogram equalization (MHE) [6], total variation [7], guided filter [8], non-local means (NLM) [9], and gradient constraint [10]. However, all of these methods have drawbacks: (1) Infrared images have fewer details than visible images, and some structural information may be lost when FPN is removed.…”

Section: Introductionmentioning

confidence: 99%

Nonuniformity Correction of Single Infrared Images Based on Deep Filter Neural Network

Jian

Wang

2018

Symmetry

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The fixed-pattern noise (FPN) caused by nonuniform optoelectronic response limits the sensitivity of an infrared imaging system and severely reduces the image quality. Therefore, nonuniform correction of infrared images is very important. In this paper, we propose a deep filter neural network to solve the problems of network underfitting and complex training with convolutional neural network (CNN) applications in nonuniform correction. Our work is mainly based on the idea of deep learning, where the nonuniform image noise features are fully learned from a large number of simulated training images. The network is designed by introducing the filter and the subtraction structure. The background interference of the image is removed by the filter, so the learning model is gathered in the nonuniform noise. The subtraction structure is used to further reduce the input-to-output mapping range, which effectively simplifies the training process. The results from the test on infrared images shows that our algorithm is superior to the state-of-the-art algorithm in visual effects and quantitative measurements, providing a new method for deep learning in nonuniformity correction of single images.

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Single image stripe nonuniformity correction with gradient-constrained optimization model for infrared focal plane arrays

Cited by 37 publications

References 14 publications

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

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

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

Nonuniformity Correction of Single Infrared Images Based on Deep Filter Neural Network

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