Relevant bibliographies by topics / Intrinsic image decomposition

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Intrinsic image decomposition'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Intrinsic image decomposition"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Intrinsic image decomposition"

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Laffont, Pierre-Yves. "Intrinsic image decomposition from multiple photographs." Nice, 2012. http://www.theses.fr/2012NICE4060.

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La modification d’éclairage et de matériaux dans une image est un objectif de longue date en traitement d’image, vision par ordinateur et infographie. Cette thèse a pour objectif de calculer une décomposition en images intrinsèques, qui sépare une photographie en composantes indépendantes : la réflectance, qui correspond à la couleur des matériaux, et l’illumination, qui représente la contribution de l’éclairage à chaque pixel. Nous cherchons à résoudre ce problème difficile à l’aide de plusieurs photographies de la scène. L’intuition clé des approches que nous proposons est de contraindre la
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Thioune, Abdoulaye. "Décomposition modale empirique et décomposition spectrale intrinsèque : applications en traitement du signal et de l’image." Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1127/document.

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Dans cette thèse, il est question d'une étude sur les méthodes d'analyse temps fréquence, temps échelle et plus particulièrement sur la décomposition modale empirique en faisant d'abord un parcours sur les méthodes traditionnelles, de l'analyse de Fourier à la transformée en ondelettes, notamment la représentation multi-résolution. Le besoin d'une précision sur les mesures aussi bien dans l'espace temporel que dans l'espace fréquentiel a toujours été une préoccupation majeure. En fait, la transformation de Fourier ne permet pas de concilier la description fréquentielle et la localisation dans
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Lee, Ching-Cheng, and 李靜澄. "GAN-based Intrinsic Image Decomposition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5q54p6.

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碩士<br>國立交通大學<br>多媒體工程研究所<br>108<br>In this thesis, we present a novel approach to perform intrinsic image decomposition on real world scenes. Intrinsic components of real world scenes are difficult to measure, so training of decomposition lacks of guidance from viable labels of real world scenes. On the other hand, acquiring intrinsic components of synthesis scenes is simple, but generating massive amount of data for training is still time-consuming. In order to achieve our goal, we utilize the structure of generative adversarial network by training generator of it to perform real world image
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LIU, KAI-TING, and 劉凱庭. "Chip and System Design of Intrinsic Image Decomposition and Enhancement Based on Conditionl Generative Adversarial Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/y7d7fc.

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碩士<br>國立臺北科技大學<br>電子工程系<br>107<br>With the advance of science and technology, various image related technologies are becoming more and more developed, and there are also vigorous developments in various fields, such as home multimedia display devices, vehicle multimedia system and equipments, digital cameras, etc.. In recent years, self-driving car’s development attracts people’s attention. The in-car display, digital camera and LiDAR mounted on the car can be combined with color images and point cloud images to detect the surrounding environment, which greatly helps the accuracy of the detect
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Hariharan, Harishwaran. "Extending Depth of Field via Multifocus Fusion." 2011. http://trace.tennessee.edu/utk_graddiss/1187.

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In digital imaging systems, due to the nature of the optics involved, the depth of field is constricted in the field of view. Parts of the scene are in focus while others are defocused. Here, a framework of versatile data-driven application independent methods to extend the depth of field in digital imaging systems is presented. The principal contributions in this effort are the use of focal connectivity, the direct use of curvelets and features extracted by Empirical Mode Decomposition, namely Intrinsic Mode Images, for multifocus fusion. The input images are decomposed into focally connected
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Book chapters on the topic "Intrinsic image decomposition"

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Ma, Wei-Chiu, Hang Chu, Bolei Zhou, Raquel Urtasun, and Antonio Torralba. "Single Image Intrinsic Decomposition Without a Single Intrinsic Image." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01264-9_13.

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Chang, Jason, Randi Cabezas, and John W. Fisher. "Bayesian Nonparametric Intrinsic Image Decomposition." In Computer Vision – ECCV 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10593-2_46.

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Tian, Jiandong. "Intrinsic-Image Deriving and Decomposition." In All Weather Robot Vision. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6429-8_5.

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Ma, Yupeng, Xiaoyi Feng, Xiaoyue Jiang, Zhaoqiang Xia, and Jinye Peng. "Intrinsic Image Decomposition: A Comprehensive Review." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71607-7_55.

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Muhammad, Siraj, Matthew N. Dailey, Imari Sato, and Muhammad F. Majeed. "Handling Specularity in Intrinsic Image Decomposition." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_13.

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Hamaen, Koumei, Daisuke Miyazaki, and Shinsaku Hiura. "Multispectral Photometric Stereo Using Intrinsic Image Decomposition." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4818-5_22.

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Yuan, Yuan, Bin Sheng, Ping Li, Lei Bi, Jinman Kim, and Enhua Wu. "Deep Intrinsic Image Decomposition Using Joint Parallel Learning." In Advances in Computer Graphics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22514-8_28.

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Li, Chen, Kun Zhou, and Stephen Lin. "Intrinsic Face Image Decomposition with Human Face Priors." In Computer Vision – ECCV 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10602-1_15.

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Liu, Yuanliu, Zejian Yuan, and Nanning Zheng. "Intrinsic Image Decomposition from Pair-Wise Shading Ordering." In Computer Vision -- ACCV 2014. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16814-2_6.

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Liu, Shirui, Hamid A. Jalab, and Zhen Dai. "Intrinsic Face Image Decomposition from RGB Images with Depth Cues." In Advances in Visual Informatics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34032-2_14.

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Conference papers on the topic "Intrinsic image decomposition"

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Hansana, Phonekham, Puhong Duan, Kaihua Xiao, Xin Guo, and Zhuojun Xie. "Intrinsic Image Decomposition-based Multi-resolution Image Fusion." In 2024 7th International Conference on Information Communication and Signal Processing (ICICSP). IEEE, 2024. https://doi.org/10.1109/icicsp62589.2024.10809127.

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Yuan, Fangzheng, Xiaoyue Jiang, Xiaoyi Feng, and Moncef Gabbouj. "Intrinsic Image Decomposition Based on Quantized Prior Codebook." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647296.

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Sato, Shogo, Takuhiro Kaneko, Kazuhiko Murasaki, Taiga Yoshida, Ryuichi Tanida, and Akisato Kimura. "Unsupervised Single-Image Intrinsic Image Decomposition with LiDAR Intensity Enhanced Training." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00236.

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Yu, Wenbo, and Miao Zhang. "Cross-modal three-dimensional intrinsic image decomposition for hyperspectral and LiDAR image joint classification." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662588.

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Tang, Fujian, Lizhi Zhao, Hong-Nan Li, et al. "Spatial Variation Analysis of Localized Corrosion of Steel Bar with Spectral Analysis Technique." In CONFERENCE 2022. AMPP, 2022. https://doi.org/10.5006/c2022-17792.

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Abstract This study aims to statistically analyze the distribution characteristics of localized corrosion along the length of corroded steel bars with spectral analysis techniques. Steel bars were embedded in a concrete prism and subjected to accelerated corrosion to levels ranging from 5.0 wt.% to 30.0 wt.% mass loss. After the corrosion test, the corroded steel bars were taken out of the concrete and cleaned with a sand blaster, and then scanned with a 3D laser scanner. The scanned point clouds were processed with an image processing software to determine the residual cross-sectional area di
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ASWATHY, V. S., and M. SAJEER. "Intrinsic Image Decomposition for Image Enhancement." In 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2018. http://dx.doi.org/10.1109/icoei.2018.8553876.

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Lin, Tzu-Heng, Pengxiao Wang, and Yizhou Wang. "Intrinsic Image Decomposition by Pursuing Reflectance Image." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/163.

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Intrinsic image decomposition is a fundamental problem for many computer vision applications. While recent deep learning based methods have achieved very promising results on the synthetic densely labeled datasets, the results on the real-world dataset are still far from human level performance. This is mostly because collecting dense supervision on a real-world dataset is impossible. Only a sparse set of pairwise judgement from human is often used. It's very difficult for models to learn in such settings. In this paper, we investigate the possibilities of only using reflectance images for sup
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Shi, Jian, Yue Dong, Xin Tong, and Yanyun Chen. "Efficient intrinsic image decomposition for RGBD images." In VRST '15: 21th ACM Symposium on Virtual Reality Software and Technology. ACM, 2015. http://dx.doi.org/10.1145/2821592.2821601.

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Xie, Dehua, Shuaicheng Liu, Kaimo Lin, Shuyuan Zhu, and Bing Zeng. "Intrinsic decomposition for stereoscopic images." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532657.

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Liu, Risheng, Cheng Yang, Long Ma, Miao Zhang, Xin Fan, and Zhongxuan Luo. "Enhanced Residual Dense Intrinsic Network for Intrinsic Image Decomposition." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00253.

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