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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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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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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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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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Fall Detection Using Probability Density Based denoising algorithm and 3D Doppler-Time Maps 9

Fall Detection Using Probability Density Based Denoising Alg...

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9th International Conference on Computer and Communication Systems (ICCCS)

作者： Ma, Zhanchao Wu, Xiaochuan Deng, Weibo Dong, Yingning Li, Wenlong Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9798350350227;9798350350210

With the increasing population of the elderly, their health problems have attracted much concern. In particular, falls are a common cause of disability and even death among older adults, and thus, fall monitoring is becoming a necessity for them. However, for the fall detection algorithms based on the Doppler time (DT) map, the factors, such as the noise and clutter, will significantly affect the detection accuracy. Therefore, we propose a denoising algorithm based on Rayleigh probability distribution for DT maps. To fuse micro-Doppler features of human movements, we then construct them as a three- dimensional (3D) matrix with the vectors near the target range bin. Rather than extract 2D features in DT maps, we put the 3D-DT maps into a lightweight neural network composed of 3D convolutional neural networks (3D-CNNs) bi-directional long short term memory (Bi-LSTM) networks designed in this paper In order to test the effect of the denoising algorithm, we collect four daily motions and three fall motions. And we find the performance of the network was significantly improved compared with mean filtering.

关键词： 3D Doppler-time map denoising algorithm deep learning fall detection millimeter-wave radar

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Loaded coal-rock temperature denoising algorithm based on CEEMD and adaptive NIWT with NIWOA

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MEASUREMENT 2024年 236卷

作者： Li, Hao Li, Xin Yang, Zhen Liu, Zhiyong Bu, Jingran Wang, Yiru Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Peoples R China

This paper examines the composition and characteristics of loaded coal-rock temperature collected directly. And a new denoising algorithm for loaded coal-rock temperature is proposed, using complete ensemble empirical mode decomposition (CEEMD) and adaptive nonlinear improved wavelet transform (NIWT) with novel improved whale optimization algorithm (NIWOA). Then the effect of the proposed algorithm's each part is discussed. Finally, the rationale and advancement of the proposed algorithm is validated with data from uniaxial experiments. It is shown that CEEMD can entirely split the original temperature into three categories: environmental component, noise-dominated mode function, and signal-dominated mode function. And in contrast to similar, the reconstruction strategy combining CEEMD and the noise similarity based on ACF is the most effective for denoising, whose results' SNR and RMSE are greater than 52 dB and less than 0.06, respectively. In addition, both NIWOA and NIWT have significantly improved the optimization speed, solving accuracy and denoising effect compared with the original. Their combination can effectively denoise the noise-dominated modal function, and the resulting SNR and RMSE can reach 53.6831 dB and 0.0281. Furthermore, the experimental analysis demonstrates that the proposed coal-rock temperature denoising algorithm has good performance and robustness, with results that are smooth and complete and INNR can be as low as 0.00968, which is clearly superior to other similar or current ones. The research can be useful for further studies of the evolution of the temperature field and the damage location method for loaded coal-rock fracture.

关键词： denoising algorithm Loaded coal-rock Temperature CEEMD Optimal wavelet threshold

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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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A HYBRID IMAGE FUSION AND denoising algorithm BASED ON MULTI-SCALE TRANSFORMATION AND SIGNAL SPARSE REPRESENTATION

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 3500-3506页

作者： Sheng, Dajun Xinyang Univ Coll Big Data & Artificial Intelligence Xinyang 464000 Henan Peoples R China

In response to the problem of denoising in image fusion, the author proposes a hybrid image fusion and denoising algorithm based on multi-scale transformation (MLT) and signal sparse representation (SRS). A hybrid model is constructed for shear transformation, and the coefficients after MLT decomposition are thresholded. Sliding window technology and translation invariance are used to form sparse representation for image fusion, and SRS algorithm is used to remove noise from the source image. The experimental results show that the algorithm reduces the contrast and spectral information distortion of the fused image, displays high-quality visual fusion effects, maintains high PSNR values under different noise levels, can provide a more complete description of the features in the image, accurately judge the focus area, maintain the structural correlation of the image, and strengthen the description of fusion edges and details in the fused image. It has been proven that the methods of multi-scale transformation and sparse signal representation can fuse and denoise images.

关键词： Multiscale transformation Signal sparsity Image fusion denoising algorithm

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A wind speed vector-wind power curve modeling method based on data denoising algorithm and the improved Transformer

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartA期214卷

作者： Zha, Wenting Jin, Ye Sun, Yalu Li, Yalong China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China State Grid Gansu Elect Power Co Econ & Technol Res Inst Lanzhou 730000 Gansu Peoples R China

The complex nonlinear relationship between the wind speed and the wind power, and the singularity of wind speed information leads to the lack of accuracy of current wind power curve modeling. To address the problem, this paper presents a high-precision wind power curve modeling method based on the wind speed vector, including the wind speeds and wind directions at different heights of the wind measuring tower. First, considering the stochastic fluctuation of the wind speed vectors and wind power sequences, complementary ensemble empirical modal decomposition with adaptive noise (CEEMDAN) is used to decompose and reconstruct the highnoise data. Second, based on the reconstructed data, dynamic time warping (DTW) is adopted to analyze the lagged causality between current wind power and historical wind speed series. In order to better mine the rules, the improved Transformer network is proposed with two convolutional layers and multi-head attention mechanisms to develop the wind speed vector-wind power curve model. Finally, through comparative experiments with the mainstream methods, the advancement of the proposed wind power curve modeling method is verified from the perspective of modeling error and its distribution

关键词： Wind turbine Power curve Improved transformer attention mechanism denoising algorithm

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A 2D image 3D reconstruction function adaptive denoising algorithm

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1604页

作者： Wang, Feng Ni, Weichuan Liu, Shaojiang Xu, Zhiming Qiu, Zemin Wan, Zhiping Guangzhou Xinhua Univ Dongguan Guangdong Peoples R China

To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image's detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target's feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction.

关键词： denoising algorithm Threshold Adversarial generative network 3D reconstruction

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