Single Infrared Image-Based Stripe Nonuniformity Correction via a Two-Stage Filtering Method

Zeng, Qingjie; Qin, Hanlin; Yang, Shuowen; Yang, Tingwu

doi:10.3390/s18124299

Cited by 15 publications

(13 citation statements)

References 28 publications

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“…(a) DLSNUC [4] (b) DMRN [7] (c) Proposed To compare the quantitative performance of our proposed method against the comparative FPN removal methods, we employed three kinds of reference-free image quality metrics. Roughness [34], root-mean-square error of the horizontal adjacent pixel (RMSE-AP) [33], and average vertical gradient error (AVGE) [36]. The roughness index can measure the high-frequency components in both horizontal and vertical directions.…”

Section: Comparative Experiments Using the Real Infrared Imagesmentioning

confidence: 99%

“…where P is the reconstructed image with m rows and n columns, P(i, j) is a pixel signal in the i-th row and the j-th column. On the other hand, Zeng et al [36] introduced the AVGE that calculates the difference of gradients between real corrupted image and its reconstructed noise-free image in the vertical direction. AVGE can assess the preserving ability on the vertical details of the image, so that we applied the AVGE as a complement to the RMSE-AP index from the perspective of quantitative evaluation of information loss.…”

Section: Comparative Experiments Using the Real Infrared Imagesmentioning

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: Comparative Experiments Using the Real Infrared Imagesmentioning

confidence: 99%

Section: Comparative Experiments Using the Real Infrared Imagesmentioning

confidence: 99%

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

Lee

2020

IEEE Access

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“…In addition to the visual contrast, we applied two objective indicators to evaluate the algorithm, image roughness index ρ and average vertical gradient error (AVGE) [30].…”

Section: Stripe Correction Algorithm Comparison Evaluationmentioning

confidence: 99%

“…The proposed algorithm (Figure 15e) and CNN algorithm ( Figure 15d) have similar results; small changes corresponding to image details are preserved, with stripe noise being simultaneously smoothed. In addition to the visual contrast, we applied two objective indicators to evaluate the algorithm, image roughness index ρ and average vertical gradient error (AVGE) [30]. To evaluate the detail protection ability of the algorithm, we introduce the value of AVGE to illustrate the detail protection ability of the algorithm.…”

Section: Stripe Correction Algorithm Comparison Evaluationmentioning

confidence: 99%

Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization

et al. 2019

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In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

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“…He et al [23] used deep residual networks to end-to-end estimate the nonuniformity. Zeng et al [24] proposed a two-stage filtering NUC method which was suitable for stripe FPN correction.…”

Section: Scene-based Nonuniformity Correctionmentioning

confidence: 99%

Temporal-Spatial Nonlinear Filtering for Infrared Focal Plane Array Stripe Nonuniformity Correction

Qin

Zeng

et al. 2019

Symmetry

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In this work, we introduce a temporal-spatial approach for infrared focal plane array (IRFPA) stripe nonuniformity correction in infrared images that generates visually appealing results. We posit that the nonuniformity appears as a striped structure in the spatial domain and that the pixel values change slowly in the temporal domain. Based on this, we formulate our correction method in two steps. In the first step, weighted guided image filtering with our adaptive weight is utilized to predict the stripe nonuniformity using a single frame. In the second step, the temporal profile of each pixel can be formed using a few frames of successive nonuniformity images. Further, we present a temporal nonlinear diffusion equation to remove scene residuals from the temporal profile of nonuniformity images in order to estimate a more accurate value of the stripe nonuniformity. The results of extensive experiments demonstrate that the proposed nonuniformity correction algorithm substantially outperforms many state-of-the-art approaches, including both traditional and deep convolution-neural-network-based methods, on four popular infrared videos. In addition, the proposed method only requires a fraction (less than ten) of the video frames.

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Single Infrared Image-Based Stripe Nonuniformity Correction via a Two-Stage Filtering Method

Cited by 15 publications

References 28 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 Analysis and Gradient Equalization

Temporal-Spatial Nonlinear Filtering for Infrared Focal Plane Array Stripe Nonuniformity Correction

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