Fourier Spectrum Guidance for Stripe Noise Removal in Thermal Infrared Imagery

Zeng, Qingjie; Qin, Hanlin; Zhou, Huixin

doi:10.1109/lgrs.2019.2938822

Cited by 19 publications

(14 citation statements)

References 17 publications

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“…Especially, in Fig. 2(b), only some active pixels in different orientations are used, which is more advantageous in processing stripe noise in the IR image (more common in images obtained by scanning devices [43]).…”

Section: B Features Of Different Types Of Componentsmentioning

confidence: 99%

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Han

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Background estimation is an efficient infrared (IR) small target detection method. However, to deal with unknown targets, the estimation window in existing algorithms should be adjusted to perform multiscale detection, and requires a lot of calculations. Besides, the stages during and after estimation have received wide attention in existing algorithms, but the research on the stages before estimation is insufficient. Moreover, existing algorithms typically regard the maximum value of different orientations as the estimation value. However, when a dim target is adjacent to high brightness background, it is easily submerged. This paper proposes a three-layer estimation window to detect targets of different sizes with only a single-scale calculation. The enhanced closest-mean background estimation method is then proposed and carefully designed before, during and after the estimation. Before estimation, the matched filter is adopted to improve the image signal-to-noise ratio. During estimation, the principle of closest-mean is proposed to suppress high brightness background. After estimation, a ratio-difference operation is performed to enhance the true target and suppress the background simultaneously. A simple checking mechanism is proposed to further improve the detection performance. Experiments on some IR images demonstrate the effectiveness and robustness of the proposed method. Compared with existing algorithms, the proposed method has better target enhancement, background suppression, and computational efficiency. Index Terms-IR small target, background estimation, matched filter, closest-mean, three-layer window.

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Section: B Features Of Different Types Of Componentsmentioning

confidence: 99%

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Han

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…As an alternative for destriping, transform domain methods that are chiefly designed and realized in the Fourier domain and wavelet domain have also received considerable attention and research [35][36][37][38][39][40][41][42][43][44]. One significant starting point of these methods is that stripe noise has a concentrated energy distribution in both domains due to its directionality and global similarity.…”

Section: Related Workmentioning

confidence: 99%

“…In [42], a multi-scale operation of guided filtering is performed on the noisy wavelet coefficients to adaptively estimate stripe noise from vertical high-frequency details. One preliminary study of ours on destriping is to correct the dominant Fourier coefficients that are contaminated by stripe noise with a reference spectrum [43]. Moreover, the idea of deep learning is being integrated into the wavelet domain to produce possibly better destriping results [35,44].…”

Section: Related Workmentioning

confidence: 99%

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

2020

Self Cite

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Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method.

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“…Visualization of thermal information benefits the recognition of the surroundings in some cases (e.g., low-light conditions or dark areas) because thermal imaging tends to provide more meaningful information in urban environments with huge thermal variations [1]. Therefore, LWIR imagers have been extensively used in medical, military, and security applications, as well as commercial applications, such as remote sensing [2,3], medical imaging [4], advanced driver-assistance systems [5], and face recognition [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Thermal Image Restoration Based on LWIR Sensor Statistics

Han

Lee

Kang

2021

Sensors

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An imaging system has natural statistics that reflect its intrinsic characteristics. For example, the gradient histogram of a visible light image generally obeys a heavy-tailed distribution, and its restoration considers natural statistics. Thermal imaging cameras detect infrared radiation, and their signal processors are specialized according to the optical and sensor systems. Thermal images, also known as long wavelength infrared (LWIR) images, suffer from distinct degradations of LWIR sensors and residual nonuniformity (RNU). However, despite the existence of various studies on the statistics of thermal images, thermal image processing has seldom attempted to incorporate natural statistics. In this study, natural statistics of thermal imaging sensors are derived, and an optimization method for restoring thermal images is proposed. To verify our hypothesis about the thermal images, high-frequency components of thermal images from various datasets are analyzed with various measures (correlation coefficient, histogram intersection, chi-squared test, Bhattacharyya distance, and Kullback–Leibler divergence), and generalized properties are derived. Furthermore, cost functions accommodating the validated natural statistics are designed and minimized by a pixel-wise optimization method. The proposed algorithm has a specialized structure for thermal images and outperforms the conventional methods. Several image quality assessments are employed for quantitatively demonstrating the performance of the proposed method. Experiments with synthesized images and real-world images are conducted, and the results are quantified by reference image assessments (peak signal-to-noise ratio and structural similarity index measure) and no-reference image assessments (Roughness (Ro) and Effective Roughness (ERo) indices).

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Fourier Spectrum Guidance for Stripe Noise Removal in Thermal Infrared Imagery

Cited by 19 publications

References 17 publications

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

Thermal Image Restoration Based on LWIR Sensor Statistics

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