Fast iterative adaptive nonuniformity correction with gradient minimization for infrared focal plane arrays

Zhao, Jincheng; Gao, Xiumin; Chen, Yueting; Feng, Hao; Xu, Zhihai; Li, Qi

doi:10.1016/j.infrared.2014.04.004

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…Recently more and more researchers are working on the design of the optimal estimation based method of NUC. It usually makes good use of the minimal energy of the image's gradient in some direction and constructs loss function between the output and desired value to constrain the FPN of IRFPA [12]- [17]. One classical method is combined the least mean square (LMS) error estimation and the neural network model, which including NN-NUC [13], BF-NUC [14], TV-NUC [15]- [16] and GF-NUC [17], and etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Infrared Focal Plane Non-Uniformity Correction Method Based on Co-Occurrence Filter and Adaptive Learning Rate

Lingxiao

Feng

et al. 2019

IEEE Access

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Non-uniformity commonly exists in the infrared focal plane, which behaves as the fixed pattern noise (FPN) and seriously affects the image quality of the infrared imaging system. This paper proposed a novel scene-based non-uniformity correction method with a new edge-preserve filter and adaptive learning rate. First, using co-occurrence filter as the desired image estimation, the proposed method removed the FPN while preserving the image details. Then, an adaptive learning rate connected with both temporal motion and spatial correlation factor is utilized to decrease the effect of ghosting artifacts. In this way, the proposed method overcomes the shortcomings of the traditional scene-based non-uniformity. Several real infrared image sequences collected in different conditions are used to verify the performance of the proposed method. The experimental results demonstrate that the proposed method has a much better visual effect, making a great balance between the non-uniformity correction and details preservation. Compared with other good NUC methods, this method also has better performance in the aspects of applicability and robustness, which has great application value.INDEX TERMS Non-uniformity correction, IRFPA, edge-preserve filter, adaptive learning rate, ghosting artifacts, details preservation.

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Section: Introductionmentioning

confidence: 99%

A Novel Infrared Focal Plane Non-Uniformity Correction Method Based on Co-Occurrence Filter and Adaptive Learning Rate

Lingxiao

Feng

et al. 2019

IEEE Access

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“…These algorithms do not consider the physical model of the imaging principle but rather improve the image quality in order to satisfy requirements by blindly enhancing the contrast of the images and highlighting image details. Algorithms in this category include histogram equalization [1], retinex theory [2], and saliency extraction [3][4][5][6]. The second category comprises algorithms that are based on image recovery [7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

An Image Dehazing Method Based On an Improved Retinex Theory

Alharbi¹,

Shan²,

et al. 2016

Proceedings of the 2016 International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICC

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Image quality is often affected in many ways by the atmosphere, especially in foggy weather conditions. Dehazing is a highly demanded operation within the domain of image processing for various applications. The paper proposes a new single-image dehazing algorithm based on the single scale retinex (SSR) algorithm, combined with the theory of atmospheric scattering. Compared with the SSR algorithm, the proposed algorithm is shown to effectively take advantage of the abundance of information within an image and offers a clearer dehazing output within distant image scenes. Compared with the histogram equalization algorithm, our algorithm achieves a higher degree of natural colour restoration and hence yields a high color fidelity. Additionally, the algorithm exhibits a higher efficiency in recovering image features from objective and subjective points of view.

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“…Image processing technologies have a wide application along with the process of image transmission, restoration, compression and reconstruction by various image devices [1,2]. For these image devices, the signal to noise ratio (SNR) index of captured image is in direct proportion to square root of exposure time.…”

Section: Introductionmentioning

confidence: 99%

The optimal code searching method with an improved criterion of coded exposure for remote sensing image restoration

Cui

Feng

et al. 2015

Optics Communications

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Fast iterative adaptive nonuniformity correction with gradient minimization for infrared focal plane arrays

Cited by 12 publications

References 16 publications

A Novel Infrared Focal Plane Non-Uniformity Correction Method Based on Co-Occurrence Filter and Adaptive Learning Rate

A Novel Infrared Focal Plane Non-Uniformity Correction Method Based on Co-Occurrence Filter and Adaptive Learning Rate

An Image Dehazing Method Based On an Improved Retinex Theory

The optimal code searching method with an improved criterion of coded exposure for remote sensing image restoration

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