Effective Strip Noise Removal for Low-Textured Infrared Images Based on 1-D Guided Filtering

Cao, Yanan; Yang, Michael Ying; Tisse, Christel-Loïc

doi:10.1109/tcsvt.2015.2493443

Cited by 78 publications

(54 citation statements)

References 37 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%

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%

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

et al. 2018

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“…In recent years, a large number of single-frame NUC algorithms have been proposed [25][26][27]. It can be divided into methods based on gray statistics [12,13,28], spatial filtering [4][5][6] and constrained optimization [29][30][31].…”

Section: Related Workmentioning

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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“…Recent years, infrared imaging systems have been extensively applied in military and civilian areas such as night version, video surveillance, driver assistance, fire detection, and disease diagnosis [1][2][3]. In these areas, infrared imaging has broad application prospects due to its ability to reflect the thermal radiation distribution of the scene, which is impossible for visual imaging.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the final enhanced image is possible to make a compromise between the merits of global and local enhancements. The main contributions of our paper are: (1) The 2D histogram is utilized to realize both global and local contrast enhancement. (2) The global enhancement result is obtained by applying the histogram specification to the clipped 2D histogram.…”

Section: Introductionmentioning

confidence: 99%

Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

Sui

Kuang

et al. 2019

Remote Sensing

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In this paper, an optimized contrast enhancement method combining global and local enhancement results is proposed to improve the visual quality of infrared images. Global and local contrast enhancement methods have their merits and demerits, respectively. The proposed method utilizes the complementary characteristics of these two methods to achieve noticeable contrast enhancement without artifacts. In our proposed method, the 2D histogram, which contains both global and local gray level distribution characteristics of the original image, is computed first. Then, based on the 2D histogram, the global and local enhanced results are obtained by applying histogram specification globally and locally. Lastly, the enhanced result is computed by solving an optimization equation subjected to global and local constraints. The pixel-wise regularization parameters for the optimization equation are adaptively determined based on the edge information of the original image. Thus, the proposed method is able to enhance the local contrast while preserving the naturalness of the original image. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the block-based methods for improving the visual quality of infrared images.

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Effective Strip Noise Removal for Low-Textured Infrared Images Based on 1-D Guided Filtering

Cited by 78 publications

References 37 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

Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

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