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Orthogonal Transform-Driven Data Augmentation for Limited Gaussian-Tainted Dataset

ieee ACCESS 2024年 12卷 127272-127282页

作者： Won Yoon, Jung Jun Yook, Hyun Min Hong, Pyo Kyu Lee, Youn Kim, Tae Hyung Hongik Univ Dept Comp Engn Seoul 04066 South Korea

A large amount of data collected from sensors exhibits Gaussian noise characteristics, making denoising and related processing critical. However, data scarcity can lead to overfitting, posing challenges in training deep learning-based denoising methods. While various data augmentation methods have been proposed, they do not provide a means to augment original data to large-scale data while preserving the exact noise distribution. To address this, we introduce a novel data augmentation method for data with additive white Gaussian noise (AWGN). Our method is based on two main premises: first, orthogonal transforms preserve the probability distribution of AWGN;second, the signals we aim to recover generally exhibit smooth characteristics, unlike noise. Building on these premises, we propose adaptive smoothness-promoting orthogonal transforms for augmenting limited existing data. We evaluated the proposed method in Gaussian denoising tasks with limited data and confirmed that it achieves substantial improvement in deep learning model performance, comparable to those obtained with sufficient data.

关键词： Data augmentation Gaussian noise smoothness-promoting orthogonal transform Data augmentation smoothness-promoting orthogonal transform Gaussian noise REFERENCES [1] A. Buades B. Coll and J.-M. Morel "A non-local algorithm for image denoising "in proc. ieee comput. soc. conf. comput. Vis. pattern Recognit. (CVPR) vol. 2 Jun. 2005 pp. 60-65. [2] K. Dabov A. Foi V. Katkovnik and K. Egiazarian "image denoising by sparse 3-D transform-domain collaborative filtering "ieee ieee Trans. image process. vol. 16 no. 8 pp. 2080-2095 Aug. 2007. [3] M. Elad and M. Aharon "image denoising via sparse and redundant representations over learned dictionaries vol. 15 no. 12 pp. 3736-3745 Dec. 2006. [4] S. V. Mohd Sagheer and S. N. George "A review on medical image denoising algorithms "Biomed. Biomed. Signal process. Control vol. 61 Aug. 2020 Art. no. 102036 [1] A. Buades "ieee Trans. image process. "Biomed. Signal process. Control

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Decoupling-and-Aggregating for image Exposure Correction

Decoupling-and-Aggregating for Image Exposure Correction

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Wang, Yang Peng, Long Li, Liang Cao, Yang Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing Peoples R China

ISBN: (纸本)9798350301298

The images captured under improper exposure conditions often suffer from contrast degradation and detail distortion. Contrast degradation will destroy the statistical properties of low-frequency components, while detail distortion will disturb the structural properties of high-frequency components, leading to the low-frequency and high-frequency components being mixed and inseparable. This will limit the statistical and structural modeling capacity for exposure correction. To address this issue, this paper proposes to decouple the contrast enhancement and detail restoration within each convolution process. It is based on the observation that, in the local regions covered by convolution kernels, the feature response of low-/high-frequency can be decoupled by addition/difference operation. To this end, we inject the addition/difference operation into the convolution process and devise a Contrast Aware (CA) unit and a Detail Aware (DA) unit to facilitate the statistical and structural regularities modeling. The proposed CA and DA can be plugged into existing CNN-based exposure correction networks to substitute the Traditional Convolution (TConv) to improve the performance. Furthermore, to maintain the computational costs of the network without changing, we aggregate two units into a single TConv kernel using structural re-parameterization. Evaluations of nine methods and five benchmark datasets demonstrate that our proposed method can comprehensively improve the performance of existing methods without introducing extra computational costs compared with the original networks. The codes will be publicly available.

关键词： computational imaging

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Attribute Group Editing for Reliable Few-shot image Generation

Attribute Group Editing for Reliable Few-shot Image Generati...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Ding, Guanqi Han, Xinzhe Wang, Shuhui Wu, Shuzhe Jin, Xin Tu, Dandan Huang, Qingming Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Key Lab Intell Info Proc Inst Comput Tech Beijing Peoples R China Huawei Cloud EI Innovat Lab Shanghai Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs). Due to the unstable GAN training process and the limited training data, the generated images are often of low quality and low diversity. In this work, we propose a new "editing-based" method, i.e., Attribute Group Editing (AGE), for few-shot image generation. The basic assumption is that any image is a collection of attributes and the editing direction for a specific attribute is shared across all categories. AGE examines the internal representation learned in GANs and identifies semantically meaningful directions. Specifically, the class embedding, i.e., the mean vector of the latent codes from a specific category, is used to represent the category-relevant attributes, and the category-irrelevant attributes are learned globally by Sparse Dictionary Learning on the difference between the sample embedding and the class embedding. Given a GAN well trained on seen categories, diverse images of unseen categories can be synthesized through editing category-irrelevant attributes while keeping category-relevant attributes unchanged. Without re-training the GAN, AGE is capable of not only producing more realistic and diverse images for downstream visual applications with limited data but achieving controllable image editing with interpretable category-irrelevant directions. Code is available at https://***/UniBester/AGE.

关键词： Training Visualization Codes image synthesis Semantics Training data Generative adversarial networks

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Automatic recognition of Learning Resource Category in a Digital Library 21

Automatic Recognition of Learning Resource Category in a Dig...

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21st ACM/ieee Joint conference on Digital Libraries (JCDL)

作者： Banerjee, Soumya Sanyal, Debarshi Kumar Chattopadhyay, Samiran Bhowmick, Plaban Kumar Das, Partha Pratim IIT Kharagpur Kharagpur 721302 W Bengal India Indian Assoc Cultivat Sci Kolkata 700032 India Jadavpur Univ Kolkata 700106 India

ISBN: (纸本)9781665417709

Digital libraries generally need to process a large volume of diverse document types. The collection and tagging of metadata is a long, error-prone, workforce-consuming task. We are attempting to build an automatic metadata extractor for digital libraries. In this work, we present the Heterogeneous Learning Resources (HLR) dataset for document image classification. The individual learning resource is first decomposed into its constituent document images (sheets) which are then passed through an OCR tool to obtain the textual representation. The document image and its textual content are classified with state-of-the-art classifiers. Finally, the labels of the constituent document images are used to predict the label of the overall document.

关键词： deep learning transfer learning digital library document image classification

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Foreword

Proceedings of the 2016 International Conference on Image Pr...

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proceedings of the 2016 International conference on image processing, computer Vision, and pattern recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

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STATISTICAL pattern recognition FOR REAL-TIME image EDGE DETECTION ON FPGA 12

STATISTICAL PATTERN RECOGNITION FOR REAL-TIME IMAGE EDGE DET...

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12th ieee International conference on Signal processing (ICSP)

作者： Liu, Ziyan Qi, Jia Feng, Liang Feng, Li Guizhou Univ Coll Elect & Informat Guiyang 550025 Peoples R China State Grid Chongqing Elect Power Co Chongqing 400014 Peoples R China

ISBN: (纸本)9781479921867

image edge detection is a fundamental process in computer vision. image edges represent the major fraction of information in an image. Traditional edge-detection techniques focus on the gradient calculation method. In this paper, for the first time, the statistical pattern recognition method is used to detect the edge after the real-time image was processed via the median filtering method and implemented on FPGA. In comparison to the Sobel algorithm, the proposed method has superior anti-noise capability.

关键词： Statistical pattern recognition Median Filtering FPGA Real-Time image Edge Detection

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proceedings of the 2012 International conference on image processing, computer Vision, and pattern recognition, IPCV 2012: Foreword

Proceedings of the 2012 International Conference on Image Pr...

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proceedings of the 2012 International conference on image processing, computer Vision, and pattern recognition, IPCV 2012 2012年 1卷

作者： Arabnia, Hamid R. Deligiannidis, Leonidas ISIBM United States Advisory Board IEEE TC on Scalable Computing University of Georgia GA United States Wentworth Institute of Technology Boston MA United States

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Multimodal image matching using self similarity

Multimodal image matching using self similarity

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2011 ieee Applied imagery pattern recognition Workshop: Imaging for Decision Making, AIPR 2011

作者： Huang, Jing You, Suya Zhao, Jiaping Department of Computer Science University of Southern California Los Angeles CA 90089 United States

ISBN: (纸本)9781467302159

This paper presents a new image description and matching process based on internal self-similarity property of images. Various definitions of self-similarity are explored to find the best one for image matching. The method also ensures rotation and scale invariance and computational efficiency through a feature detection process. Experiments demonstrate that the proposed method increases robustness of image matching under different imaging conditions or modalities. © 2011 ieee.

关键词： image matching

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image registration and a metric to assess the transformation function

Image registration and a metric to assess the transformation...

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2011 ieee Applied imagery pattern recognition Workshop: Imaging for Decision Making, AIPR 2011

作者： Marshall, John Doucette, Peter NGA 7500 GEOINT Drive Springfield VA 22150 United States

ISBN: (纸本)9781467302159

image registration has been a broadly applied topic across the photogrammetric/remote sensing and computer vision communities. It is a foundational step for many applications such geopositioning, data fusion, change detection, conflation, and object recognition and extraction. The efficacy of many automated geospatial processes can be limited or nullified by an inadequate registration process. The task of automated image registration presents two main challenges: 1) establishing image-to-image correspondence through feature matching, and 2) determining an appropriate transformation model for a given registration scenario. When imaging 3D environments, a goal of the transformation function is to accurately relate the 2D pixel spaces of candidate images with potential geometric distortions and surface discontinuities projected from a 3D object space. When sensor model metadata and 3D surface information is available (e.g. a digital surface model), a 3D-to-2D photogrammetric transformation will generally provide the most reliable registration solution. Moreover, photogrammetric solutions propagate error to provide a statistically rigorous estimation of registration accuracy. On the other hand, direct 2D-to-2D transformations such as affine, homographic, and polynomials are often used when sensor metadata and/or object space information is limited or unavailable. Owing to their convenience of use and implementation, direct 2D-to-2D registration methods abound in commercial software application. However, such registration solutions are generally more suspect in terms of accuracy and uncertainty estimation. Nonetheless, they do have practical utility, provided appropriate care is exercised in their application. The goal of this paper is to quantitatively demonstrate different scenarios and solutions that users should consider when applying 3D-to-2D photogrammetric versus direct 2D-to-2D image registration methods. © 2011 ieee.

关键词： image registration

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Fusion of Elevation Data into Satellite image Classification Using Refined Production Rules 1

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8th International conference on image Analysis and recognition (ICIAR) / 2nd International conference on Autonomous and Intelligent Systems (AIS)

作者： Al Momani, Bilal Morrow, Philip McClean, Sally Cisco Syst Galway Ireland Univ Ulster Fac Engn Sch Comput & Informat Engn Coleraine BT52 1SA Londonderry North Ireland

ISBN: (数字)9783642215933

ISBN: (纸本)9783642215926;9783642215933

The image classification process is based on the assumption that pixels which have similar spatial distribution patterns, or statistical characteristics, belong to the same spectral class. In a previous study we have shown how we can improve the accuracy of classification of remotely sensed imagery data by incorporating contextual elevation knowledge in a form of a digital elevation model with the output of the classification process using Dempster-Shafer Theory of Evidence. A knowledge based approach is created for this purpose using suitable production rules derived from the elevation distributions and range of values for the elevation data attached to a particular satellite image. Production rules are the major part of knowledge representation and have the basic form: IF condition THEN Inference. Although the basic form of production rules has shown accuracy improvement, in general, in some cases accuracy can degrade. In this paper we propose a "refined" approach that takes into account the actual "distribution" of elevation values for each class rather than simply the "range" of values to solve the accuracy degradation. This approach is performed by refining the basic production rules used in the previous study taking into account the number of pixels at each elevation within the elevation distribution for each class.

关键词： Remote sensing classification evidence theory

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