Single Infrared Image Stripe Noise Removal Using Deep Convolutional Networks

Kuang, Xiaodong; Sui, Xiubao; Chen, Qian; Gu, Guohua

doi:10.1109/jphot.2017.2717948

Cited by 78 publications

(51 citation statements)

References 20 publications

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“…In the experiments, both synthesized images and real noise corrupted remote sensing images were tested, and we compared the proposed model with several typical state-of-the-art destriping methods, including the spatial domain filter method based on guided filter (GF-based) [40], the frequency domain filter method wavelet-Fourier filtering method (WAFT) [15], the unidirectional variational based models, including the UV method [9], HUTV method [19], sparse UV model (SUV) [24] and convolutional neural network based method stripe noise removal convolutional neural network (SNRCNN) [31]. The traditional denoising method block-matching and 3D filtering (BM3D) [41] are also selected to be compared.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Some researchers also exploited the high spectral correlation property among the different bands in hyperspectral data to recover the latent image [28][29][30]. With the rapid development of deep learning techniques, the deep convolutional networks based destriping methods [31] were proposed and showed a competitive stripe noise removal ability in the infrared image. However, their framework was designed for weak stripe noise only, and could not be suitable to the strong stripe noise.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

et al. 2018

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Stripe noise removal continues to be an active field of research for remote image processing. Most existing approaches are prone to generating artifacts in extreme areas and removing the stripe-like details. In this paper, a weighted double sparsity unidirectional variation (WDSUV) model is constructed to reduce this phenomenon. The WDSUV takes advantage of both the spatial domain and the gradient domain's sparse property of stripe noise, and processes the heavy stripe area, extreme area and regular noise corrupted areas using different strategies. The proposed model consists of two variation terms and two sparsity terms that can well exploit the intrinsic properties of stripe noise. Then, the alternating direction method of multipliers (ADMM) optimal solver is employed to solve the optimization model in an alternating minimization scheme. Compared with the state-of-the-art approaches, the experimental results on both the synthetic and real remote sensing data demonstrate that the proposed model has a better destriping performance in terms of the preservation of small details, stripe noise estimation and in the mean time for artifacts' reduction.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

et al. 2018

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“…Chang et al [32] proposed to consider the characteristics of strip noise and the spectral information of remote sensing images at the same time, and use the stripe noise problem as an image decomposition problem naturally. Kuang et al [33] proposed a deep convolutional neural network (CNN) model to correct the non-uniformity in a single-frame infrared image, which remove stripe noise effect is better.…”

Section: Related Workmentioning

confidence: 99%

“…We test the algorithm on raw infrared image, and give quantitative comparison, qualitative analysis and parameter discussion. In the experiment, three state-of-the-art stripe removal methods were compared, namely midway histogram equalization (MHE) [13], L-model algorithm [10] and CNN [33]. All parameter variables of these comparison algorithms are set to the default values using the corresponding references.…”

Section: Experiments and Analysismentioning

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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“…The main advantage of NN is their ability to learn, which allows the network to automatically study hidden patterns in input data sets. NN work with complex nonlinear dependencies, solving multi-parameter problems [13,14]. This al- where nnumber of vector elements (counts in the signal).…”

Section: Neural Networkmentioning

confidence: 99%

Analysis of Application of Neural Networks to Improve the Reliability of Active Thermal NDT

Galagan

Momot

2019

KPI SN

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Background. The relevant question of increasing the informative content and reliability of the thermal non-destructive testing is considered in this article. The most promising algorithms of digital processing of sequences of thermograms are given. Objective. The main aim of this research is to determine the advantages and disadvantages of the application of each considered method of digital processing of thermograms. Secondary, the possibilities of testing automation with the use of the selected methods of digital processing of thermograms are analyzed in this article. Methods. Computer simulation software was used to obtain the artificial sequence of the thermograms. Methods of wavelet analysis, principal components analysis and neural networks were used to process the received data. Results. The simulation of active thermal testing process is carried out in this research. The artificial thermogram sequence with a high level of noise is obtained for the object of testing. In order to quantify the results of application of considered methods, relative errors of determining the area of defects were calculated. Also values of Tanimoto criterion are obtained. The advantages of the neural network processing of digital data in thermal non-destructive testing have been established and proved in this article. Shape of defects on a binary map built by the neural network was closest to true compared with principal components analysis method. The effectiveness of neural networks is also confirmed by quantitative estimates. Conclusions. The method of wavelet transformation has a high sensitivity. This method is ineffective in the conditions of uneven heating and high noise. The principal components analysis method allows increasing the SNR and improving the visual perception of thermograms, but does not provide complete separation of information about defects and noises caused by uneven heating. Methods of artificial neural networks theory provide the best reproduction of the shape and size of the defects, but the training process requires significant time and computing resources.

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Single Infrared Image Stripe Noise Removal Using Deep Convolutional Networks

Cited by 78 publications

References 20 publications

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

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

Analysis of Application of Neural Networks to Improve the Reliability of Active Thermal NDT

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