Variational model with kernel metric-based data term for noisy image segmentation

Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic strips of missing data in the imagery bands. Within the vast and interdisciplinary image reconstruction application field, many works have been presented in the last few decades to tackle the specific Landsat 7 gap-filling problem. This work proposes another contribution in this field presenting an original procedure based on a variational image segmentation model coupled with radiometric analysis to reconstruct damaged images acquired in a multi-temporal scenario, typical in satellite remote sensing. The key idea is to exploit some specific features of the Mumford–Shah variational model for image segmentation in order to ease the detection of homogeneous regions which will then be used to form a set of coherent data necessary for the radiometric reconstruction of damaged regions. Two reconstruction approaches are presented and applied to SLC-off Landsat 7 data. One approach is based on the well-known histogram matching transformation, the other approach is based on eigendecomposition of the bands covariance matrix and on the sampling from Gaussian distributions. The performance of the procedure is assessed by application to artificially damaged images for self-validation testing. Both of the proposed reconstruction approaches had led to remarkable results. An application to very high resolution WorldView-3 data shows how the procedure based on variational segmentation allows an effective reconstruction of images presenting a great level of geometric complexity.

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Shannon Wavelet Precision Integration Method for Pathologic Onion Image Segmentation Based on Homotopy Perturbation Technology

Mathematical Problems in Engineering ◽

10.1155/2014/601841 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Haihua Wang ◽

Shu-Li Mei

Keyword(s):

Image Segmentation ◽

Burkholderia Cepacia ◽

Integration Method ◽

Interpolation Property ◽

Variational Model ◽

Nonlinear Odes ◽

Precise Integration ◽

Homotopy Perturbation ◽

Analytical Scheme ◽

Shannon Wavelet

Image segmentation variational method is good at processing the images with blurry and complicated contours, which is useful in quality identification of pathologic picture of onion. An adaptive Shannon wavelet precise integration method (WPIM) on digital image segmentation was proposed based on the image processing variational model to improve the processing speed and eliminate the artifacts of the images. First, taking full advantage of the interpolation property of the Shannon wavelet function, a multiscale Shannon wavelet interpolation scheme was constructed based on the homotopy perturbation method (HPM). The image pixels of the Burkholderia cepacia (ex-Burkholder) infected onions were taken as the collocation points of the WPIM. Then, with this scheme, the image segmentation model (C-V model) can be discretized into a system of nonlinear ODEs and solved by the half-analytical scheme combining the HPM and the precision integration method. At last, the numerical precision and efficiency of WPIM were discussed and compared with other common segmentation methods such as OSTU method and Sobel operator. The results show that the contour curve of the segmentation object obtained by the new method has many excellent properties such as closed and clear topological structure and the artifacts can be eliminated.

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Globally convex variational model for multiphsae image segmentation

2015 Chinese Automation Congress (CAC) ◽

10.1109/cac.2015.7382572 ◽

2015 ◽

Author(s):

Huaxiang Liu ◽

Jiangxiong Fang ◽

Liting Zhang ◽

Huaxiang Liu ◽

Jing Xiao ◽

...

Keyword(s):

Image Segmentation ◽

Variational Model

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Local Kernel-Induced Fitting Variational Model for MRI Image Segmentation

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2016.1783 ◽

2016 ◽

Vol 6 (4) ◽

pp. 1013-1019

Author(s):

Huaxiang Liu ◽

Jiangxiong Fang ◽

Liting Zhang ◽

Jun Liu ◽

Zhengjun Zeng

Keyword(s):

Image Segmentation ◽

Variational Model ◽

Mri Image

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Integrating guided filter into fuzzy clustering for noisy image segmentation

Digital Signal Processing ◽

10.1016/j.dsp.2018.08.022 ◽

2018 ◽

Vol 83 ◽

pp. 235-248 ◽

Cited By ~ 14

Author(s):

Li Guo ◽

Long Chen ◽

C.L. Philip Chen ◽

Jin Zhou

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Guided Filter ◽

Noisy Image

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A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation

Journal of Visual Communication and Image Representation ◽

10.1016/j.jvcir.2019.01.001 ◽

2019 ◽

Vol 59 ◽

pp. 89-107 ◽

Cited By ~ 15

Author(s):

Cheng Liu ◽

Weibin Liu ◽

Weiwei Xing

Keyword(s):

Image Segmentation ◽

Level Set Method ◽

Level Set ◽

Statistical Information ◽

Noisy Image ◽

Edge Based

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Medical Imaging Importance in the Real World

Advances in Computer and Electrical Engineering - Challenges and Applications for Implementing Machine Learning in Computer Vision ◽

10.4018/978-1-7998-0182-5.ch006 ◽

2020 ◽

pp. 136-162

Author(s):

Ramgopal Kashyap

Keyword(s):

Image Segmentation ◽

Edge Detection ◽

Level Set ◽

Real World ◽

Medical Image ◽

Automatic Segmentation ◽

Medical Image Segmentation ◽

Image Resolution ◽

Variational Model ◽

Medical Applications

In the medical image resolution, automatic segmentation is a challenging task, and it's still an unsolved problem for most medical applications due to the wide variety connected with image modalities, encoding parameters, and organic variability. In this chapter, a review and critique of medical image segmentation using clustering, compression, histogram, edge detection, parametric, variational model. and level set-based methods is presented. Modes of segmentation like manual, semi-automatic, interactive, and automatic are also discussed. To present current challenges, aim and motivation for doing fast, interactive and correct segmentation, the medical image modalities X-ray, CT, MRI, and PET are discussed in this chapter.

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