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Journal articles on the topic 'Intrinsic image decomposition'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Xu, Chen, Yu Han, George Baciu, and Min Li. "Fabric image recolorization based on intrinsic image decomposition." Textile Research Journal 89, no. 17 (2018): 3617–31. http://dx.doi.org/10.1177/0040517518817051.

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Fabric image recolorization is widely used in assisting designers to generate new color design proposals for fabric. In this paper, a new image recolorization method is proposed. Different from classical image recolorization methods, which need some complicated interactive operations from users, our proposed method can achieve automatic recolorization of images. The proposed method contains three sequential phases: a phase of extracting representative colors from fabric images; an image segmentation phase; and an image reconstruction phase by using given color themes. Integrated with intrinsic

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2

Yu, Yuanhui. "Intrinsic Decomposition Method Combining Deep Convolutional Neural Network and Probability Graph Model." Computational Intelligence and Neuroscience 2022 (February 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/4463918.

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With the rapid development of computer vision and artificial intelligence, people are increasingly demanding image decomposition. Many of the current methods do not decompose images well. In order to find the decomposition method with high accuracy and accurate recognition rate, this study combines convolutional neural network and probability map model, and proposes a single-image intrinsic image decomposition method that is on both standard dataset images and natural images. Compared with the existing single-image automatic decomposition algorithm, the visual effect comparable to the user int

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Jin, Xudong, and Yanfeng Gu. "Superpixel-Based Intrinsic Image Decomposition of Hyperspectral Images." IEEE Transactions on Geoscience and Remote Sensing 55, no. 8 (2017): 4285–95. http://dx.doi.org/10.1109/tgrs.2017.2690445.

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Baslamisli, Anil S., Partha Das, Hoang-An Le, Sezer Karaoglu, and Theo Gevers. "ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition." International Journal of Computer Vision 129, no. 8 (2021): 2445–73. http://dx.doi.org/10.1007/s11263-021-01477-5.

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AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neura

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Kang, Xudong, Shutao Li, Leyuan Fang, and Jon Atli Benediktsson. "Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images." IEEE Transactions on Geoscience and Remote Sensing 53, no. 4 (2015): 2241–53. http://dx.doi.org/10.1109/tgrs.2014.2358615.

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6

Yue, Huanjing, Jingyu Yang, Xiaoyan Sun, Feng Wu, and Chunping Hou. "Contrast Enhancement Based on Intrinsic Image Decomposition." IEEE Transactions on Image Processing 26, no. 8 (2017): 3981–94. http://dx.doi.org/10.1109/tip.2017.2703078.

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7

Hauagge, Daniel, Scott Wehrwein, Kavita Bala, and Noah Snavely. "Photometric Ambient Occlusion for Intrinsic Image Decomposition." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 4 (2016): 639–51. http://dx.doi.org/10.1109/tpami.2015.2453959.

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8

Chen, Xi, Weixin Zhu, Yang Zhao, et al. "Intrinsic decomposition from a single spectral image." Applied Optics 56, no. 20 (2017): 5676. http://dx.doi.org/10.1364/ao.56.005676.

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9

Sidibe, Samba, Oumar Niang, Abdoulaye Thioune, Abdoul-Dalibou Abdou, and Ndeye Fatou Ngom. "The 2D Spectral Intrinsic Decomposition Method Applied to Image Analysis." Journal of Electrical and Computer Engineering 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/7948571.

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We propose a new method for autoadaptive image decomposition and recomposition based on the two-dimensional version of the Spectral Intrinsic Decomposition (SID). We introduce a faster diffusivity function for the computation of the mean envelope operator which provides the components of the SID algorithm for any signal. The 2D version of SID algorithm is implemented and applied to some very known images test. We extracted relevant components and obtained promising results in images analysis applications.

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10

MATSUOKA, Ryo, Tatsuya BABA, Mia RIZKINIA, and Masahiro OKUDA. "White Balancing by Using Multiple Images via Intrinsic Image Decomposition." IEICE Transactions on Information and Systems E98.D, no. 8 (2015): 1562–70. http://dx.doi.org/10.1587/transinf.2015edp7070.

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11

LINDERHED, ANNA. "IMAGE EMPIRICAL MODE DECOMPOSITION: A NEW TOOL FOR IMAGE PROCESSING." Advances in Adaptive Data Analysis 01, no. 02 (2009): 265–94. http://dx.doi.org/10.1142/s1793536909000138.

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Image empirical mode decomposition (IEMD) is an empirical mode decomposition concept used in Hilbert–Huang transform (HHT) expanded into two dimensions for the use on images. IEMD provides a tool for image processing by its special ability to locally separate superposed spatial frequencies. The tendency is that the intrinsic mode functions (IMFs) other than the first are low-frequency images. In this study we give an overview of the state-of-the-art methods to decompose an image into a number of IMFs and a residue image with a minimum number of extrema points, together with the use of the meth

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12

Yi, Renjiao, Ping Tan, and Stephen Lin. "Leveraging Multi-View Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12685–92. http://dx.doi.org/10.1609/aaai.v34i07.6961.

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We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images. To do so, we construct a multi-view dataset by collecting numerous customer product photos online, which exhibit large illumination variations that make them suitable for training of reflectance separation and can facilitate object-level decomposition. The main contribution of our approach is a proposed image representation based on local color distributions that allows training to be insensitive to the local misalignments of multi-view images. In

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13

Careaga, Chris, and Yağız Aksoy. "Colorful Diffuse Intrinsic Image Decomposition in the Wild." ACM Transactions on Graphics 43, no. 6 (2024): 1–12. http://dx.doi.org/10.1145/3687984.

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Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. Due to the complexity of the problem, most prior works assume a single-color illumination and a Lambertian world, which limits their use in illumination-aware image editing applications. In this work, we separate an input image into its diffuse albedo, colorful diffuse shading, and specular residual components. We arrive at our result by gradually removing first the single-color illumination and then the Lambertian-world assumptions. We show that by dividing t

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14

Baslamisli, Anil S., Yang Liu, Sezer Karaoglu, and Theo Gevers. "Physics-based shading reconstruction for intrinsic image decomposition." Computer Vision and Image Understanding 205 (April 2021): 103183. http://dx.doi.org/10.1016/j.cviu.2021.103183.

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15

Gu, Yanfeng, Wen Xie, Xian Li, and Xudong Jin. "Hyperspectral Intrinsic Image Decomposition With Enhanced Spatial Information." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–14. http://dx.doi.org/10.1109/tgrs.2022.3146063.

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16

Jianbing Shen, Xiaoshan Yang, Xuelong Li, and Yunde Jia. "Intrinsic Image Decomposition Using Optimization and User Scribbles." IEEE Transactions on Cybernetics 43, no. 2 (2013): 425–36. http://dx.doi.org/10.1109/tsmcb.2012.2208744.

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17

Krebs, Alexandre, Yannick Benezeth, and Franck Marzani. "Intrinsic image decomposition as two independent deconvolution problems." Signal Processing: Image Communication 86 (August 2020): 115872. http://dx.doi.org/10.1016/j.image.2020.115872.

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18

Choi, Wonhyeok, Kyumin Hwang, Minwoo Choi, et al. "Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 2555–63. https://doi.org/10.1609/aaai.v39i3.32258.

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Self-supervised monocular depth estimation (SSMDE) has gained attention in the field of deep learning as it estimates depth without requiring ground truth depth maps. This approach typically uses a photometric consistency loss between a synthesized image, generated from the estimated depth, and the original image, thereby reducing the need for extensive dataset acquisition. However, the conventional photometric consistency loss relies on the Lambertian assumption, which often leads to significant errors when dealing with reflective surfaces that deviate from this model. To address this limitat

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19

Du, Jiao, Weisheng Li, and Heliang Tan. "Intrinsic Image Decomposition-Based Grey and Pseudo-Color Medical Image Fusion." IEEE Access 7 (2019): 56443–56. http://dx.doi.org/10.1109/access.2019.2900483.

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20

Nadian-Ghomsheh, Ali, Yassin Hassanian, and Keyvan Navi. "Intrinsic Image Decomposition via Structure-Preserving Image Smoothing and Material Recognition." PLOS ONE 11, no. 12 (2016): e0166772. http://dx.doi.org/10.1371/journal.pone.0166772.

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21

Lettry, L., K. Vanhoey, and L. Van Gool. "Unsupervised Deep Single‐Image Intrinsic Decomposition using Illumination‐Varying Image Sequences." Computer Graphics Forum 37, no. 7 (2018): 409–19. http://dx.doi.org/10.1111/cgf.13578.

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22

Diop, El Hadji S., Radjesvarane Alexandre, and Lionel Moisan. "Intrinsic nonlinear multiscale image decomposition: A 2D empirical mode decomposition-like tool." Computer Vision and Image Understanding 116, no. 1 (2012): 102–19. http://dx.doi.org/10.1016/j.cviu.2011.09.003.

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23

Zhao, Kun, Jisheng Ding, YanFei Sun, and ZhiYuan Hu. "Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means." E3S Web of Conferences 206 (2020): 03019. http://dx.doi.org/10.1051/e3sconf/202020603019.

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In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental qu

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24

Beigpour, Shida, Sumit Shekhar, Mohsen Mansouryar, Karol Myszkowski, and Hans-Peter Seidel. "Light-Field Appearance Editing based on Intrinsic Decomposition." Journal of Perceptual Imaging 1, no. 1 (2018): 10502–1. http://dx.doi.org/10.2352/j.percept.imaging.2018.1.1.010502.

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Abstract The authors present a framework for image-based surface appearance editing for light-field data. Their framework improves over the state of the art without the need for a full “inverse rendering,” so that full geometrical data, or presence of highly specular or reflective surfaces are not required. It is robust to noisy or missing data, and handles many types of camera array setup ranging from a dense light field to a wide-baseline stereo-image pair. They start by extracting intrinsic layers from the light-field image set maintaining consistency between views. It is followed by decomp

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25

LINDERHED, ANNA. "VARIABLE SAMPLING OF THE EMPIRICAL MODE DECOMPOSITION OF TWO-DIMENSIONAL SIGNALS." International Journal of Wavelets, Multiresolution and Information Processing 03, no. 03 (2005): 435–52. http://dx.doi.org/10.1142/s0219691305000932.

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Previous work on empirical mode decomposition in two dimensions typically generates a residue with many extrema points. In this paper we propose an improved method to decompose an image into a number of intrinsic mode functions and a residue image with a minimum number of extrema points. We further propose a method for the variable sampling of the two-dimensional empirical mode decomposition. Since traditional frequency concept is not applicable in this work, we introduce the concept of empiquency, shortform for empirical mode frequency, to describe the signal oscillations. The very special pr

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26

Sheng, Bin, Ping Li, Yuxi Jin, Ping Tan, and Tong-Yee Lee. "Intrinsic Image Decomposition with Step and Drift Shading Separation." IEEE Transactions on Visualization and Computer Graphics 26, no. 2 (2020): 1332–46. http://dx.doi.org/10.1109/tvcg.2018.2869326.

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27

Gong, Wenyong, Weihong Xu, Leqin Wu, Xiaohua Xie, and Zhanglin Cheng. "Intrinsic Image Sequence Decomposition Using Low-Rank Sparse Model." IEEE Access 7 (2019): 4024–30. http://dx.doi.org/10.1109/access.2018.2888946.

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28

Shen, Li, Chuohao Yeo, and Binh-Son Hua. "Intrinsic Image Decomposition Using a Sparse Representation of Reflectance." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 12 (2013): 2904–15. http://dx.doi.org/10.1109/tpami.2013.136.

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29

Ren Zhiwei, 任智伟, and 吴玲达 Wu Lingda. "Hyperspectral Intrinsic Image Decomposition Based on Automatic Subspace Partitioning." Laser & Optoelectronics Progress 55, no. 10 (2018): 103004. http://dx.doi.org/10.3788/lop55.103004.

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30

Ding, Shouhong, Bin Sheng, Xiaonan Hou, Zhifeng Xie, and Lizhuang Ma. "Intrinsic Image Decomposition Using Multi-Scale Measurements and Sparsity." Computer Graphics Forum 36, no. 6 (2016): 251–61. http://dx.doi.org/10.1111/cgf.12874.

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31

Nadian-Ghomsheh, Ali, Yasin Hasanian, and Keyvan Navi. "Correction: Intrinsic Image Decomposition via Structure-Preserving Image Smoothing and Material Recognition." PLOS ONE 12, no. 2 (2017): e0171949. http://dx.doi.org/10.1371/journal.pone.0171949.

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32

Xing, Guanyu, Yanli Liu, Wanfa Zhang, and Haibin Ling. "Light mixture intrinsic image decomposition based on a single RGB-D image." Visual Computer 32, no. 6-8 (2016): 1013–23. http://dx.doi.org/10.1007/s00371-016-1238-8.

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33

Guo, Zhenwei. "A blind color watermarking technique based on quaternion complex structure-preserving Qr decomposition." Eurasian Journal of Mathematical and Computer Applications 12, no. 1 (2024): 57–69. http://dx.doi.org/10.32523/2306-6172-2024-12-1-57-69.

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The color image blind watermarking technique is widely used in the field of digital image processing and security and can maintain the copyright of images and guarantee their authenticity. In order to maintain the intrinsic connection between the three color channels of color images, using quaternion algebra to solve the color image problems often achieves better results. This paper proposes a complex structure-preserving algorithm for solving the QR decomposition of quaternion matrices, which has better computational efficiency compared with previous methods. This paper also applies the propo

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Xing, Xiaoyan, Konrad Groh, Sezer Karaoglu, and Theo Gevers. "Intrinsic-GS: Multi-view Intrinsic Image Decomposition Using Gaussian Splatting and Color-Invariant Priors." Color and Imaging Conference 32, no. 1 (2024): 56–63. https://doi.org/10.2352/cic.2024.32.1.12.

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35

Laffont, P., A. Bousseau, and G. Drettakis. "Rich Intrinsic Image Decomposition of Outdoor Scenes from Multiple Views." IEEE Transactions on Visualization and Computer Graphics 19, no. 2 (2013): 210–24. http://dx.doi.org/10.1109/tvcg.2012.112.

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Liu, Jianxin, Yupeng Ma, Xi Meng, Shan Zhang, Zhiguo Liu, and Yufei Song. "Enhancing Intrinsic Image Decomposition with Transformer and Laplacian Pyramid Network." Traitement du Signal 41, no. 1 (2024): 511–17. http://dx.doi.org/10.18280/ts.410146.

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37

Chen, Hai Peng, Xuan Jing Shen, and You Wei Wang. "Blind Digital Watermarking Algorithm Based on SVD Decomposition." Applied Mechanics and Materials 40-41 (November 2010): 610–18. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.610.

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In order to balance the algorithm’s robustness and the watermark’s transparency better, a blind digital watermarking algorithm based on SVD decomposition was proposed. First, divided the original image into a number of 8*8 size blocks; then execute DCT transform on each block; next, execute SVD decomposition on each 2*2 size block of DC coefficients set with the purpose of get-ting the corresponding singular value matrix; finally, complete the watermark embedding by quali-fying the largest singular value using a step value Q based on the principle of parity quantification. The algorithm extrac

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38

Zheng, Rongjia, Qing Zhang, Yongwei Nie, and Wei-Shi Zheng. "When Shadow Removal Meets Intrinsic Image Decomposition: A Joint Learning Framework Using Unpaired Data." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10599–607. https://doi.org/10.1609/aaai.v39i10.33151.

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We present a framework that achieves shadow removal by learning intrinsic image decomposition (IID) from unpaired shadow and shadow-free images. Although it is well-known that intrinsic images, \ie, illumination and reflectance, are highly beneficial to shadow removal, IID is rarely adopted by previous work due to its inherent ambiguity and the scarcity of training data. However, we find that by properly coupling shadow removal and IID into a joint learning framework, they can reinforce each other and enable promising results on both tasks, even with unpaired training data. Our framework is co

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Li, Lizhao, Song Xiao, and Yimin Zhao. "Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization." Sensors 20, no. 19 (2020): 5666. http://dx.doi.org/10.3390/s20195666.

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This paper focuses on image compressive sensing (CS). As the intrinsic properties of natural images, nonlocal self-similarity and sparse representation have been widely used in various image processing tasks. Most existing image CS methods apply either self-adaptive dictionary (e.g., principle component analysis (PCA) dictionary and singular value decomposition (SVD) dictionary) or fixed dictionary (e.g., discrete cosine transform (DCT), discrete wavelet transform (DWT), and Curvelet) as the sparse basis, while single dictionary could not fully explore the sparsity of images. In this paper, a

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ZHANG, MIN, and YI SHEN. "ENSEMBLE EMPIRICAL MODE DECOMPOSITION FOR HYPERSPECTRAL IMAGE CLASSIFICATION." Advances in Adaptive Data Analysis 04, no. 01n02 (2012): 1250003. http://dx.doi.org/10.1142/s1793536912500033.

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Ensemble empirical mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. This paper presents the utilization of EEMD for hyperspectral images to extract signals from them, generated in noisy nonlinear and nonstationary processes. First, EEMD is applied to each hyperspectral image band and defines the true intrinsic mode function (IMF) components as the mean of an ensemble of trials, each consisting of the signal plus a white noise of finite amplitude. After EEMD

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41

Yang, Liuqing, Wei Meng, and Xudong Zhao. "Color Image Encryption Using Angular Graph Fourier Transform." International Journal of Digital Crime and Forensics 13, no. 3 (2021): 59–82. http://dx.doi.org/10.4018/ijdcf.20210501.oa5.

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In this paper, an angular graph Fourier transform (AGFT) is introduced to encrypt color images with their intrinsic structures. The graph Fourier transform (GFT) is extended to the AGFT and proven to have the desired properties of angular transform and graph transform. In the proposed encryption method, color images are encoded by DNA sequences and confused under the control of chaotic key streams firstly. Secondly, sparse decomposition based on the random walk is applied to scramble pixels spatially, and a series of sub-images are obtained. This step increases encryption efficiency. Finally,

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Guo, Xiao-Ting, Xu-Jie Duan, and Hui-Hua Kong. "Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm." Applied Sciences 14, no. 17 (2024): 7764. http://dx.doi.org/10.3390/app14177764.

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Multi-focal image and multi-modal image fusion technology can fully take advantage of different sensors or different times, retaining the image feature information and improving the image quality. A multi-source image fusion algorithm based on bidimensional empirical mode decomposition (BEMD) and a region sharpness-guided region overlapping algorithm are studied in this article. Firstly, source images are decomposed into multi-layer bidimensional intrinsic mode functions (BIMFs) and residuals from high-frequency layer to low-frequency layer by BEMD. Gaussian bidimensional intrinsic mode functi

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BHUIYAN, SHARIF M. A., NII O. ATTOH-OKINE, KENNETH E. BARNER, ALBERT Y. AYENU-PRAH, and REZA R. ADHAMI. "BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION USING VARIOUS INTERPOLATION TECHNIQUES." Advances in Adaptive Data Analysis 01, no. 02 (2009): 309–38. http://dx.doi.org/10.1142/s1793536909000084.

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Scattered data interpolation is an essential part of bidimensional empirical mode decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and the lower envelopes, respectively. The number of two-dimensional intrinsic mode functions resulting from the decomposition and their properties are highly dependent on the method of interpolation. Though a few methods of interpolation have been tested and/or applied to the BEMD process, many others remain to be tested. This paper evaluates

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XIE Bin, 谢. 斌., 黄. 安. HUANG An, and 黄. 辉. HUANG Hui. "Colorimage denoising algorithm based on intrinsic image decomposition and sparse representation." Chinese Journal of Liquid Crystals and Displays 34, no. 11 (2019): 1104–14. http://dx.doi.org/10.3788/yjyxs20193411.1104.

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45

Ge, Yufei, Xiaoli Zhang, Bo Huang, Xiongfei Li, and Siwei Ma. "A deep unfolding network based on intrinsic image decomposition for pansharpening." Knowledge-Based Systems 308 (January 2025): 112764. http://dx.doi.org/10.1016/j.knosys.2024.112764.

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46

Song, S., and R. Qin. "A NOVEL INTRINSIC IMAGE DECOMPOSITION METHOD TO RECOVER ALBEDO FOR AERIAL IMAGES IN PHOTOGRAMMETRY PROCESSING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 23–30. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-23-2022.

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Abstract. Recovering surface albedos from photogrammetric images for realistic rendering and synthetic environments can greatly facilitate its downstream applications in VR/AR/MR and digital twins. The textured 3D models from standard photogrammetric pipelines are suboptimal to these applications because these textures are directly derived from images, which intrinsically embedded the spatially and temporally variant environmental lighting information, such as the sun illumination, direction, causing different looks of the surface, making such models less realistic when used in 3D rendering un

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47

Wei, Qi, and Qi Liu. "Denoise PET Images Based on a Combining Method of EMD and ICA." Advanced Materials Research 981 (July 2014): 340–43. http://dx.doi.org/10.4028/www.scientific.net/amr.981.340.

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The incidental component in addition to the measured target signals is considered as noise of Positron Emission Tomography (PET) images. A novel method to denoise the PET images based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) associated with Sparse Code Shrinkage (SCS) technique is proposed in this paper. EMD is executed to decompose a PET image into a number of Intrinsic Mode Functions (IMFs), which are used to reconstruct a new PET image after chosen by means of an inverse EMD procedure. By applying ICA to the new PET image, an orthogonal dataset can be o

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48

Taime, Abderazzak, Aziz Khamjane, Jamal Riffi, and Hamid Tairi. "Improving the accuracy of the PET/MRI tridimensional multimodal rigid image registration based on the FATEMD." Radioelectronic and Computer Systems, no. 1 (March 7, 2023): 122–33. http://dx.doi.org/10.32620/reks.2023.1.10.

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The subject matter of the article is the improvement in the accuracy of multimodal image registration between PET and MRI images in the medical field. The focus of the article pertains to the importance of these images in diagnosis, interpretation, and surgical intervention. This study increased the accuracy of PET/MRI multimodal image registration achieved through a new approach based on the multi-resolution image decomposition. The tasks to be solved are: The study proposes a new method, the fast and adaptive three-dimensional mode decomposition (FATEMD), to generate multi-resolution compone

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Lu, Yi, Chenyang Huang, Jia Wang, and Peng Shang. "An Improved Quantitative Analysis Method for Plant Cortical Microtubules." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/637183.

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The arrangement of plant cortical microtubules can reflect the physiological state of cells. However, little attention has been paid to the image quantitative analysis of plant cortical microtubules so far. In this paper, Bidimensional Empirical Mode Decomposition (BEMD) algorithm was applied in the image preprocessing of the original microtubule image. And then Intrinsic Mode Function 1 (IMF1) image obtained by decomposition was selected to do the texture analysis based on Grey-Level Cooccurrence Matrix (GLCM) algorithm. Meanwhile, in order to further verify its reliability, the proposed text

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Arrazaki, Mohammed, Abdelouahed Sabri, Mohamed Zohry, and Tarek Zougari. "Adaptive image watermarking using bidimensional empirical mode decomposition." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 2955–63. http://dx.doi.org/10.11591/eei.v12i5.4688.

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Digital watermarking is considered one of the technological means used to guarantee the security and authenticity of data transmitted over communication systems. A new method of image watermarking using the bidimensional empirical mode decomposition (BEMD) will be presented in this article, where the new idea is to use the BEMD of both the cover image and the watermark image. The embedding process consists of adding to each intrinsic modal function (IMF) of the cover image the corresponding IMF of the watermark image. The watermarked image contains three different watermarks and appropriate fr

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