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Improvement in panchromatic-guided denoising algorithm for research on Beijing SDGSAT-1 luminous remote sensing image denoising algorithm

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INTERNATIONAL JOURNAL OF DIGITAL EARTH 2025年第1期18卷

作者： Zhang, Lihan Bai, Linyan Li, Ruren Feng, Jianzhong Wang, Penglong Chen, Chao Shenyang Jianzhu Univ Sch Transportat & Surveying Engn Shenyang Peoples R China Hainan Aerosp Informat Res Inst Key Lab Earth Observat Hainan Prov Wenchang Peoples R China Int Res Ctr Big Data Sustainable Dev Goals 9 Dengzhuang South Rd Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing Peoples R China Chinese Acad Agr Sci Agr Informat Inst Beijing Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Lanzhou Peoples R China Jiangsu Ocean Univ Sch Marine Technol & Geomatics Lianyungang Peoples R China

The Glimmer Imaging Unit (GIU) onboard Sustainable Development Science Satellite 1 (SDGSAT-1) provides higher-resolution nighttime image data for the study of remote sensing images, but the noise in the images can have a negative impact on experimental research. In this study, based on existing denoising algorithms, the OTSU threshold method is combined with a convolution window to identify noisy pixels. The spectral correlation between the low-gain and high-gain panchromatic (PL and PH) and color (RGB) bands is used to remove noisy pixels and improve the quality of nighttime light images. Beijing is used as an example in this study. In the bands with RNE (residual noise entropy) and MRD (relative mean deviation) values close to 0, the denoising rate is maintained at about 80% in the full color and RGB bands. A comparison with the results of other commonly used denoising algorithms also shows its great advantages. The RNE value in each band is stable below 0.45, and the MRD value is 0. This study aimed to optimize the denoising process of nighttime remote sensing images through this method, providing clearer and more reliable data resources for research and applications in related fields.

关键词： SDGSAT-1 nighttime image OTSU denoising algorithm

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denoising algorithm FOR THE PIXEL-RESPONSE NON-UNIFORMITY CORRECTION OF A SCIENTIFIC CMOS UNDER LOW LIGHT CONDITIONS 23

DENOISING ALGORITHM FOR THE PIXEL-RESPONSE NON-UNIFORMITY CO...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Hu, Changmiao Bai, Yang Tang, Ping Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100101 Peoples R China

We present a denoising algorithm for the pixel-response non-uniformity correction of a scientific complementary metal-oxide-semiconductor (CMOS) image sensor, which captures images under extremely low-light conditions. By analyzing the integrating sphere experimental data, we present a pixel-by-pixel flat-field denoising algorithm to remove this fixed pattern noise, which occur in low-light conditions and high pixel response readouts. The response of the CMOS image sensor imaging system to the uniform radiance field shows a high level of spatial uniformity after the denoising algorithm has been applied.

关键词： CMOS denoising algorithm Pixel-response non-uniformity

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ESL-SELO:A Robust Image denoising algorithm with Penalty

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 753-770页

作者： Guo-hua WANG San-guo ZHANG Peng-jie DAI School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing100049China School of Business Remin University of ChinaBeijing 100872China

Robust image recovery methods have been attracted more and more attention in recent decades for its good property of tolerating system errors or measuring noise. In this paper, we propose a new robust method （ESL-SELO） to recover nosing image, which combine exponential loss function and seamless-L0 （SELO） penalty function to guarantee both accuracy and robustness of the estimator. Theoretical result showed that our method has a local optimal solution and good asymptotic properties. Finally, we compare our method with other methods in simulation which shows better robustness and takes much less time.

关键词： denoising algorithm penalty function robust SELO

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Noise removal in medical mammography images using fast non-local means denoising algorithm for early breast cancer detection: a phantom study

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OPTIK 2019年 180卷 569-575页

作者： Lee, Sungtaek Park, Seong Jin Jeon, Ji Min Lee, Mi-Hwa Ryu, Dae Yeon Lee, Eunbyeol Kang, Seong-Hyeon Lee, Youngjin Eulji Univ Dept Radiol Sci 553 Sanseong Daero Seongnam Si Gyeonggi Do South Korea Ewha Womans Univ Dept Social Studies Educ 52 Ewhayeodae Gil Seoul South Korea Kyung Hee Univ hosp Gangdong Dept Radiol 892 Dongnam Ro Seoul South Korea Gachon Univ Dept Radiol Sci 191 Hambakmoero Incheon South Korea

denoising plays a crucial role in the field of medical imaging in regard to the improvement of image quality. In this study, a fast nonlocal means (FNLM) denoising algorithm which utilizes neighborhood filtering is proposed and implemented for early breast cancer detection based on medical mammography. For comparison with conventional denoising methods, the Wiener filter and total variation (TV) denoising algorithm were used. The temporal resolution, coefficient of variation (COV), and contrast to noise ratio (CNR) were evaluated for three exposure conditions: (a) various tube voltages at fixed 40 mAs, (b) various tube currents at fixed 28 kVp, and (c) auto exposure control mode. The results showed that the proposed FNLM denoising algorithm can achieve a similar temporal resolution to the Wiener filter and may efficiently reduce image noise by using COV and CNR values in mammography.

关键词： Medial mammography Medical application denoising algorithm Fast non-local means approach Image processing

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Research on Yarn Diameter and Unevenness Based on an Adaptive Median Filter denoising algorithm

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FIBRES & TEXTILES IN EASTERN EUROPE 2020年第1期28卷 36-41页

作者： Wang, Xiao Hou, Ru-Meng Gao, Xiao-Yan Xin, Bin-Jie Yantai Nanshan Univ Nanshan Middle Rd Yantai 265713 Shandong Peoples R China

In this paper an adaptive median filtering denoising algorithm is proposed to measure yarn diameter and its unevenness. Images of nine different yarn samples were captured using one set of a self-developed yarn image acquisition system. Image separation of the background and yarn sections was conducted using a combination of adaptive median filtering, adaptive threshold segmentation and morphological processing. The noise-five yarn image was used for diameter detection of the subsequent yarn image and the discrimination of the yarn unevenness. Experimental results show that the testing data of yarn unevenness detection based on the adaptive median, filter denoising algorithm is very consistent with the data using the traditional method. It is proved that the yarn detection method proposed, based on an adaptive median filter denoising algorithm, is feasible. It can be used to calculate yarn diameter accurately and measure yam unevenness efficiently, so as to determine the quality of yarn appearance objectively.

关键词： adaptive median filtering denoising algorithm yarn diameter unevenness image processing

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High-Accurate Robust Total Variation denoising algorithm With Adjustable Exponential Upper Bound Function for Micro-Thrust Measurement

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Liu, Zhikang Chen, Xingyu Xu, Jiawen Zhang, Chengxin Xu, Luxiang Guo, Ning Zhao, Liye Yan, Ruqiang Southeast Univ Sch Instrument Sci & Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Minist Educ Key Lab Microinertial Instruments & Adv Nav Techno Nanjing 210096 Peoples R China Univ Chinese Acad Sci Sch Phys & Photoelect Engn Hangzhou 310024 Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Sch Fundamental Phys & Math Sci Key Lab Gravitat Wave Precis Measurement Zhejiang Hangzhou 310024 Peoples R China Lanzhou Inst Phys Sci & Technol Vacuum Technol & Phys Lab Lanzhou 730010 Peoples R China Xi An Jiao Tong Univ Int Machinery Ctr Sch Mech Engn Xian 710049 Shanxi Peoples R China

Micro-Newton thrusters are widely utilized in the field of astronautics. Typically, micro-Newton thrust signal processing aims at the restoration of piecewise constant signals (PCSs). Improving the amplitude accuracy of the denoised signal remains a major difficulty. In this research, we proposed a new nonlinear total variation denoising (TVD) algorithm with an adjustable majorization-minimization (MM) upper bound function for optimizing the iterative solution process of TVD. The influences of the upper bound function of the TVD on denoising are analyzed in detail. It is demonstrated that the proposed adjustable parameter -a-based TVD (aTVD) method shows extraordinary robustness for denoising signals with different noise levels. In addition, this algorithm can also adjust the sensitivity of the jumps in signals for optimized amplitude accuracy effectively. Numerical examples show that the proposed method has 40.4% and 18.2% accuracy improvement over conventional TVD using traditional quadratic upper bound and enhanced TVD using exponential upper bound function. Moreover, the advantages of the proposed method are validated in an experiment of measured thrust signal processing. The proposed method can be further adopted to improve the performance of signal denoising algorithms developed from the TVD method.

关键词： Noise reduction Upper bound Gravitational waves TV Signal processing algorithms Nonlinear filters Geologic measurements denoising algorithm majorization-minimization (MM) algorithm thrust measurement total variation (TV) upper bound

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Dictionary learning-based denoising algorithm with expected patch log likelihood in diffusion-weighted magnetic resonance image

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JOURNAL OF THE KOREAN PHYSICAL SOCIETY 2024年第9期85卷 763-771页

作者： Kim, Kyuseok Jeong, Hyun-Woo Lee, Youngjin Eulji Univ Dept Biomed Engn Seongnam Si 13135 South Korea Gachon Univ Dept Radiol Sci Incheon 21936 South Korea

Diffusion-weighted imaging (DWI) is one of the most sensitive techniques to noise among magnetic resonance imaging (MRI) techniques. As the b-value used to acquire the DWI image increases, an image in which the difference in diffusion is emphasized can be obtained. However, DWI images with increased b-values inevitably have a major drawback in that noise is amplified. Thus, in this study, a dictionary learning (DL)-based denoising algorithm was modeled and applied to DWI images. The designed algorithm was modeled as a DL-based algorithm using the expected patch log likelihood. The DWI images were obtained by adjusting the b-value from 400 to 400 intervals. When the proposed DL-based denoising algorithm was applied to DWI, we confirmed that the contrast-to-noise ratio and coefficient of variation were improved by approximately 4.26 and 5.22 times, respectively, compared with noisy images. In conclusion, we expect that the proposed DL-based denoising algorithm will be highly efficient in acquiring DWI images using a high b-value, which is useful for observing acute cerebral infarction and microvascular disease.

关键词： Diffusion-weighted imaging (DWI) Magnetic resonance imaging (MRI) denoising algorithm Dictionary-based denoising technique Quantitative evaluation of image quality

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A WAVELET PACKET TREE denoising algorithm FOR IMAGES OF ATOMIC-FORCE MICROSCOPY

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ASIAN JOURNAL OF CONTROL 2018年第4期20卷 1367-1378页

作者： Schimmack, Manuel Mercorelli, Paolo Leuphana Univ Lueneburg Inst Prod & Proc Innovat Volgershall 1 D-21339 Luneburg Germany

A threshold-free denoising procedure of acquired discrete Atomic-force microscopy (AFM) signals using the discrete wavelet transform (DWT) method is presented in this article. The integration of a denoising procedure into a control structure is extremely important for each kind of system to be controlled. The detection of unavoidable measurement noise in the acquired data of the AFM signal is done by using orthogonal wavelets (Daubechies and Symmlet) and with different polynomial approximation order for each family. The proposed denoising algorithm, based on the free wavelet toolboxes from the WaveLab 850 library of the Stanford University (USA), compares the usefulness of Daubechies and Symmlet wavelets with different vanishing moments. With the help of a seminorm the noise of a sequence is defined as a coherent and incoherent part of the AFM signal. In the first step of the procedure the algorithm analyzes the frequency subspaces of the wavelet packets tree and searches for small or opposing components in the wavelet domains. In the second step of the procedure the incoherent components in the low- and high frequency domains are localized and the incoherent is then removed from the AFM signal. The proposed algorithm structure is used to improve the quality of the AFM signals and it can be easily integrated into the existing AFM control hard- and software structures. The effectiveness of the proposed denoising algorithm is validated with real measurements.

关键词： Atomic-force microscopy denoising algorithm orthogonal wavelets wavelet packets transform method

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Enhancing SDGSAT-1 night light images using a panchromatic guidance denoising algorithm

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 128卷

作者： Wang, Ning Hu, Yonghong Li, Xiao-Ming Lan, Yiling Kang, Chuanli Yan, Lin Dou, Changyong Miao, Chen Hainan Aerosp Technol Innovat Ctr Wenchang 571333 Peoples R China Hainan Aerosp Informat Res Inst Key Lab Earth Observat Hainan Prov Wenchang 571399 Peoples R China Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541004 Peoples R China Guangxi Modern Polytech Coll Hechi 547000 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Natl Remote Sensing Ctr China Beijing 100036 Peoples R China

The Glimmer Imager on board the Sustainable Development Science Satellite 1 (SDGSAT-1) is capable of providing high-resolution nighttime light images. However, the presence of stripe noise and dark current noise in L4A images affects the authenticity of image information, thus limiting its scientific applications. This study proposes a panchromatic guidance denoising algorithm to enhance the quality of nighttime light images based on the spectral consistency properties between the panchromatic and RGB channels. This algorithm utilizes two lownoise panchromatic bands to obtain a denoised light area and subsequently removes a significant amount of noise from non-light areas in the RGB bands based on this light area. Our results indicate that this algorithm can effectively remove noise from SDGSAT-1 nighttime light images, achieving a noise removal rate greater than 99.8% in the selected dark ocean area. It also preserves more reasonable radiometric characteristics and spatial details of high-spatial resolution nighttime light images when compared to other popular denoising algorithms. The perturbation caused by moonlight can be effectively eliminated using this algorithm, which further improves the comparability and application of SDGSAT-1 nighttime light images under different lunar phases.

关键词： SDGSAT-1 Strip Noise Nighttime Light Images denoising algorithm

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Edges and hues keeping denoising algorithm for color images

Edges and hues keeping denoising algorithm for color images

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Conference on AOPC - Optical Sensing, Imaging, and Display Technology

作者： Lv, Zhixue Xue, Fenfen Qian, Weixian Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China Shanghai Aerosp Control Technol Inst 1555 Zhongchun Rd Shanghai 201109 Peoples R China

ISBN: (纸本)9781510661905;9781510661912

Based on the unique color separation characteristics of HSI space, noise removal of color images contaminated by high noise is investigated. The color image is first projected into the color feature space HSI, and the color information is separated from the luminance feature information, and then the color components H and S in this space are processed by the distance threshold denoising method in polar coordinates to remove the noise without distorting the color. At the same time, the luminance feature component I is guided filtered to effectively remove the noise while preserving the edge information of the original image;finally, the corresponding inverse conversion is performed to obtain the final color image. The algorithm has some improvement in eliminating color noise, maintaining hue, and protecting image details compared with previous algorithms, which is more in line with the visual requirements of human eyes.

关键词： HSI color space color image denoising algorithm guided filter

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