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structure-texture image decomposition via non-convex total generalized variation and convolutional sparse coding

VISUAL COMPUTER 2023年第3期39卷 1121-1136页

作者： Wang, Chunxue Xu, Linlin Liu, Ligang Dunhuang Acad Jiuquan Gansu Peoples R China Inner Mongolia Univ Finance & Econ Sch Comp Informat Management Hohhot Inner Mongolia Peoples R China Univ Sci & Technol China Sch Math Sci Hefei Anhui Peoples R China

image decomposition is a fundamental but challenging ill-posed problem in image processing and has been widely applied to compression, enhancement, texture removal, etc. In this paper, we introduce a novel structure-texture image decomposition model via non-convex total generalized variation regularization (NTGV) and convolutional sparse coding (CSC). NTGV aims to characterize the detailed-preserved structural component ameliorating the staircasing artifacts existing in total variation-based models, and CSC aims to characterize image fine-scale textures. Moreover, we incorporate both structure-aware and texture-aware measures to well distinguish structural and textural component. The proposed model is numerically implemented by an alternating minimization scheme based on alternating direction method of multipliers. Experimental results demonstrate the effectiveness of our approach on several applications including texture removal, high dynamic range image tone mapping, detail enhancement and non-photorealistic abstraction.

关键词： structure-texture image decomposition Non-convex total generalized variation regularization Convolutional sparse coding Alternating minimization scheme Detail-preserving

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structure-texture image decomposition using a new non-local TV-Hilbert model

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IET image PROCESSING 2020年第11期14卷 2525-2531页

作者： Lv, Yehu Hebei Univ Technol Inst Math Tianjin 300401 Peoples R China

Combining the advantages of the non-local total variation (TV) and the Gabor function, a new Gabor function based non-local TV-Hilbert model is presented to separate the structure and texture components of the image. Computationally, by introducing the dual form of the non-local TV, the authors reformulate the non-local TV-Hilbert minimisation problem into a convex-concave saddle-point problem. In the aspect of solving algorithm, by transforming the Chambolle-Pock's first-order primal-dual algorithm into a different equivalent form. The authors propose a proximal-based primal-dual algorithm to solve the convex-concave saddle-point problem. At last, experimental results demonstrate that the proposed new model outperforms several existing state-of-the-art variational models.

关键词： variational techniques image texture convex programming minimisation image denoising proximal-based primal-dual algorithm Chambolle-Pock's first-order primal-dual algorithm convex-concave saddle-point problem nonlocal TV-Hilbert minimisation problem texture components Gabor function nonlocal total variation nonlocal TV-Hilbert model structure-texture image decomposition

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structure-texture image decomposition Using Deep Variational Priors

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IEEE TRANSACTIONS ON image PROCESSING 2019年第6期28卷 2692-2704页

作者： Kim, Youngjung Ham, Bumsub Do, Minh N. Sohn, Kwanghoon Agcy Def Dev Inst Def Adv Technol Res Daejeon 34060 South Korea Yonsei Univ Sch Elect & Elect Engn Seoul 120749 South Korea Univ Illinois Dept Elect & Comp Engn Urbana IL 61820 USA

Most variational formulations for structure-texture image decomposition force the structure images to have small norm in some functional spaces and to share a common notion of edges, i.e., large-gradients or large-intensity differences. However, such a definition makes it difficult to distinguish structure edges from oscillations that have fine spatial scale but high contrast. In this paper, we introduce a new model by learning deep variational priors for structure images without explicit training data. An alternating direction method of a multiplier algorithm and its modular structure are adopted to plug deep variational priors into an iterative smoothing process. The central observations are that convolution neural networks (CNNs) can replace the total variation prior, and are indeed powerful to capture the natures of structure and texture. We show that our learned priors using CNNs successfully differentiate high-amplitude details from structure edges, and avoid halo artifacts. Different from previous data-driven smoothing schemes, our formulation provides another degree of freedom to produce continuous smoothing effects. Experimental results demonstrate the effectiveness of our approach on various computational photography and image processing applications, including texture removal, detail manipulation, HDR tone-mapping, and non-photorealistic abstraction.

关键词： structure-texture image decomposition total variation adaptive neighborhood filtering alternating direction method of multiplier algorithm texture filtering

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Optical flow estimation based on the structure-texture image decomposition

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SIGNAL image AND VIDEO PROCESSING 2015年第1-Sup期9卷 193-201页

作者： Bellamine, I. Tairi, H. Univ Sidi Mohamed Ben Abdellah Fac Sci Dhar El Mahraz LIIAN Dept Comp Sci Fes Morocco

Optical flow approaches for motion estimation calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. image motion estimation is a fundamental issue in low-level vision and is used in many applications in image sequence processing, such as robot navigation, object tracking, image coding and structure reconstruction. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. Actually, several methods are used to estimate the optical flow, but a good compromise between computational cost and accuracy is hard to achieve. This work presents a combined local-global total variation approach with structure-texture image decomposition. The combination is used to control the propagation phenomena and to gain robustness against illumination changes, influence of noise on the results and sensitivity to outliers. The resulted method is able to compute larger displacements in a reasonable time.

关键词： Motion estimation structure-texture image decomposition Optical flow Local-global total variation approach

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Motion estimation using the total variation-local-global optical flow and the structure- texture image decomposition

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2016年第1期53卷 41-50页

作者： Bellamine, Insaf Tairi, Hamid Sidi Mohamed Ben Abdellah Univ Dept Comp Sci LIAN BP 1796 Fes Morocco

Motion estimation is currently approximated by the visual displacement field called optical flow. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. Actually, several methods are used to estimate the optical flow, but a good compromise between computational cost and accuracy is hard to achieve. This work presents a combined local-global-total variation (CLG-TV) approach with structure-texture image decomposition. The combination is used to control the propagation phenomena and to gain robustness against illumination changes, influence of texture on the results and sensitivity to outliers. The resulting method is able to compute larger displacements in a reasonable time.

关键词： motion estimation structure-texture image decomposition optical flow local-global-total variation approach

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Detecting Motion using the structure-texture image decomposition and Space-Time Interest Points

Detecting Motion using the Structure-Texture Image Decomposi...

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8th International Conference on Intelligent Systems - Theories and Applications (SITA)

作者： Bellamine, I. Tairi, H. Fac Sci Dhar El Mahraz Dept Math & Comp LIIAN Fes Morocco

ISBN: (纸本)9781479902996

Among all the features which can be extracted from videos, we propose to use Space-Time Interest Points (STIP), these ones are particularly interesting because they are simple and robust. They allow a good characterization of a set of regions of interest corresponding to moving objects in a three-dimensional observed scene. In this paper, we propose to apply the algorithm of the detection of spatiotemporal interest points for a good detection of moving objects on both components of the decomposition: a geometric structure component and a texture component. Proposed results are obtained from very different types of videos, namely sport videos and animation movies.

关键词： Motion Space-Time Interest Points structure-texture image decomposition

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Detecting Motion using the structure-texture image decomposition and Space-Time Interest Points

Detecting Motion using the Structure-Texture Image Decomposi...

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International Conference on Intelligent Systems: Theories and Applications

作者： I.Bellamine H.Tairi LIIAN Department of mathematics and computer Faculty of Sciences Dhar El mahraz BP 1796 FES Morocco

ISBN: (纸本)9781479902972

关键词： Motion Space-Time Interest Points structure-texture image decomposition

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Motion detection based on the combining of the background subtraction and the structure-texture decomposition

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OPTIK 2015年第24期126卷 5992-5997页

作者： Elharrouss, Omar Moujahid, Driss Tairi, Hamid Univ Sidi Mohamed Ben Abdellah LIIAN Dept Informat Fac Sci Dhar Mahraz PB 1796 Atlas Fez Morocco

To detect the moving objects in a video sequence based on background subtraction approaches, a background model should be estimated at the first time before subtract it from each image of the sequence and then segmenting the moving objects. In this paper, we present a new approach based on the combining of the background subtraction and the structure-texture decomposition (BS-STD). First, each gray-level image of the sequence will be decomposed on two components, structure and texture/noise by applying the Osher and Vese algorithm. The structure component of each image of the sequence will be taken to generate the background model using the median filter. The absolute difference used to subtracting the background before compute the binary image of the moving objects using the threshold generated by Otsu's method. The structure-texture decomposition (STD) is also used to ameliorate the results of methods, the background estimation algorithm with Sigma - Delta (SDE), Simple Statistical Difference (SSD) and Motion detection with pyramid structure of background model (MDPS). The experimental results demonstrate that our approach is effective and accurate for moving objects detection and the structure-texture decomposition adds improvements to the results of state-of-the-art methods. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Motion detection Background subtraction Background model Video surveillance structure-texture image decomposition

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Motion Detection and Tracking using Space-Time Interest Points

Motion Detection and Tracking using Space-Time Interest Poin...

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IEEE and Arab-Computing-Society (ACS) International Conference on Computer Systems and Applications (AICCSA)

作者： Bellamine, I. Tairi, H. Mohamed Ben Abdellah Univ LIIAN Dept Math & Comp Fac Sci Dhar El Mahraz Fes Morocco

ISBN: (纸本)9781479907922

Space-Time Interest Points (STIP) are among all the interesting features which can be extracted from videos;they are simple, robust and they allow a good characterization of a set of regions of interest corresponding to moving objects in a three-dimensional observed scene. In this paper, we show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for tracking. For a good detection of moving objects, we propose to apply the algorithm of the detection of spatiotemporal interest points on both components of the decomposition which is based on a partial differential equation (PDE): a geometric structure component and a texture component. Proposed results are obtained from very different types of videos, namely sport videos and animation movies.

关键词： Space-Time Interest Points structure-texture image decomposition Motion Detection Tracking

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Representation of texture structures with topological data analysis for stage IA lung adenocarcinoma in three-dimensional thoracic CT images

Representation of texture structures with topological data a...

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Conference on Medical Imaging - Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Kawata, Y. Niki, N. Kusumoto, M. Ohmatsu, H. Aokage, K. Ishii, G. Matsumoto, Y. Tsuchida, T. Eguchi, K. Kaneko, M. Tokushim Univ Dept Opt Sci & Technol Tokushima Japan Nat Canc Ctr Hosp Tokyo Japan Abashiri Prison Med Affairs Sect Abashiri Japan Natl Canc Ctr Hosp East Kashiwa Chiba Japan Natl Canc Ctr Exploratory Oncol Res & Clin Trial Ctr Div Pathol Tokyo Japan Teikyo Univ Sch Med Tokyo Japan Tokyo Hlth Serv Assoc Tokyo Japan

ISBN: (纸本)9781510640306

Noninvasive biomarkers that capture the adenocarcinoma aggressiveness could provide crucial quantitative information for precision medicine to aid clinical decision making. texture features are known to measure tumor heterogeneity and have been identified as the features having a potential correlation to outcomes in lung adenocarcinomas. Nevertheless, current methods for analyzing texture patterns that arise from local intensity variation are limited to reveal a spatial configuration of the texture structures in 3D thoracic CT images. This lack of an intuitive visualization of the texture of nodules makes understanding the meanings underlying the tumor heterogeneity a challenging problem. In this study, we propose an approach combining a structure-texture image decomposition with a topological data analysis to represent a spatial configuration of the texture of lung adenocarcinoma. The image decomposition aims to split the 3D thoracic CT image into two components, namely, the structure component with the piecewise-smooth part having the global structural information of nodule and the texture component with the locally-patterned oscillating part. We demonstrate the use of topological data analysis to capture architectural features that arise from the texture component. Specifically, using persistent homology of texture components, we compute topological representations of lung adenocarcinomas with the appearance of consolidation on CT images. Applying the method to an example of early-stage lung adenocarcinomas graded with texture features based on the popular algorithm such as gray-level co-occurrence matrix (GLCM), we present that the structure-texture image decomposition model with topological data analysis might be a promising tool in analyzing the tumor heterogeneity in 3D thoracic CT images.

关键词： lung adenocarcinoma tumor heterogeneity texture structure-texture image decomposition topological data analysis 3D thoracic CT

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