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

Liu, Yang; He, Chu; Wu, Yongfei

doi:10.1016/j.dsp.2018.01.017

Cited by 25 publications

(16 citation statements)

References 36 publications

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“…In this section, various experiments tested on synthetic, natural, synthetic aperture radar (SAR) and oil spill images are shown to validate the effectiveness and robustness of the proposed model in noisy image segmentation. Classical models, e.g., CV [23] and LS [34], and the state of the art models, e.g., IRLSM [36], VMKM [41], and BLSVM [42], are taken as baseline models to compare and provide a performance evaluation. All experiments are performed on a computer with 3.30GHz Intel Core i5 CPU and 8GB RAM, purely datadriven and use a same initialized contour curve.…”

Section: Resultsmentioning

confidence: 99%

“…Wu et al [40] proposes a novel region-based model by incorporating kernel metric and fuzzy logic, which can detect the boundaries precisely and work well on the images in the presence of noise, outliers and low contrast. Liu et al [41] proposes a robust variational model through involving the kernel metric based on the Gaussian radial basis function, which can adaptively emphasize the contribution close to the mean intensity value inside (or outside) the evolving curve. Liu et al [42] proposes a binary level set variational model to segment the images with impulse noise, in which the contour is implicitly represented by a binary level set function and the energy functional is consists of a data term defined by L 1 -norm metric, a regularization term defined by Dirichlet energy of level set function and a penalty term punishing the deviation of level set function from binary function.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

Liu

Xing

2020

IEEE Access

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In image processing and computer vision, image segmentation plays a fundamental role since it can make images easier to analyze. However, noise is easily introduced into images and brings great challenges to image segmentation. This paper focuses on the segmentation problem of noisy images and proposes an efficient variational level set model based on adaptive local fitted image to handle it. By utilizing normalized local entropy and local means, an adaptive local fitted image is proposed and introduced into the data term to enhance the robustness of the model against noise. Then a penalty term is proposed to reduce the deviation of the adaptive local fitted image from the original image by punishing the difference between them, so as to guarantee the accuracy of segmentation results. Later, the total variational regularization term is introduced into the model, so the level set function can be smoothed and the effect of noise on the active contour can be further reduced. The energy functional of the whole model is convex rigorously, which can reach the minimum and should have good properties in noisy image segmentation. Numerous experiments on synthetic, natural, synthetic aperture radar and oil spill images demonstrate that the proposed model is strongly robust to different types and levels of noise, which indicates its good performance in noisy image segmentation. INDEX TERMS Adaptive local fitted image, convex model, image segmentation, noisy image, variational level set.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

Liu

Xing

2020

IEEE Access

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“…Such as Jung et al [34] used l 1 -norm metric as the data fidelity term to process the image with salt and pepper noise; the models in [35][36][37] used the weighted l 2 -norm metric to segment the image with intensity inhomogeneity. Inspired by the kernel metric methods (such as kernel support vector machine) in machine learning, Wu et al [38] and Liu et al [39] replaced the l 2 Euclidean distance by a kernel metric based on Gaussian radial basis function (GRBF) in the LBF model, called KLBF. Compared to the traditional LBF model, KLBF is more robust to the noises.…”

Section: Introductionmentioning

confidence: 99%

A variational level set model with kernel metric induced local image fitting energy

Yan

Tang

Ren

et al. 2022

IET Image Processing

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Active contour based methods are effective models for image segmentation. However, they always suffer from the limited performance due to the presence of noise and intensity inhomogeneity. To solve this problem, a kernel metric induced local image fitting (KLIF) variational model is proposed in this paper. Firstly, a kernel metric induced local fitting image (KLFI) is introduced by minimising a kernel metric based energy. The combination of the kernel metric and the local fitting image enables the model to be more robust to the noise and intensity inhomogeneity. And then, using the KLFI, a variational level set model that is a squared l 2 distance between the KLFI and the original image is constructed. Two regularisation terms are employed in the model to keep the level set function to be stable during the evolution. At last, an alternating iterative algorithm combining with fixed-point iteration and gradient descent of three-step time-splitting is introduced to solve the proposed model. The experimental results show the effectiveness of the proposed model for image segmentation in the presence of noise and intensity inhomogeneity, and demonstrate the competitive performance over several state-of-the-art variational models in term of accuracy and robustness.

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“…Defogging is, thus, becoming an increasingly desirable technique for both computational photography and computer vision tasks, and it has been extensively studied. Many studies [1–37] have made significant progress in the single‐image defogging field. The early approaches focused on image enhancement [6–9], whereas the later approaches developed handcrafted features based on the statistics of clear images, such as Colour‐Lines [15], DCP [16], and boundary constraint [29], methods.…”

Section: Introductionmentioning

confidence: 99%

Image defogging based on amended dark channel prior and 4‐directional L₁regularisation

Yang

Long

et al. 2021

IET Image Processing

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The dark channel prior (DCP) algorithm has been widely used in the field of image defogging because of its simple theory and clear restoration result. However, the DCP algorithm has significant limitations. This study clarifies the relationship between halo artfacts and the size of the dark channel patch of the DCP algorithm and analyses the reason why the colour of close‐range white objects appears distorted in the restored images. An amended DCP method is then proposed to solve these problems, utilising a locally variable weighted 4‐directional L1 regularisation and a corresponding parallel algorithm to optimise the transmission. A deep neural network, 4DL1R‐net, is then trained to further enhance the processing speed. Extensive experiments demonstrate that this method is effective. The proposed method can obtain clear details, maintain the natural clarity of images, and achieve significant improvements over state‐of‐the‐art methods.

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

Cited by 25 publications

References 36 publications

An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

A variational level set model with kernel metric induced local image fitting energy

Image defogging based on amended dark channel prior and 4‐directional L₁regularisation

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

Cited by 25 publications

References 36 publications

An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

An Efficient Variational-Level-Set Model Based on Adaptive Local Fitted Image for Noisy Image Segmentation

A variational level set model with kernel metric induced local image fitting energy

Image defogging based on amended dark channel prior and 4‐directional L1regularisation

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Image defogging based on amended dark channel prior and 4‐directional L₁regularisation