Image segmentation algorithm by piecewise smooth approximation

Wang, Yan; He, Chu

doi:10.1186/1687-5281-2012-16

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…Image smoothing, a fundamental technology in image processing and computer vision, aims to clean images by retaining salient structures (to the structureonly image) and removing insignificant textures (to the texture-only image), with various applications including denoising [1], detail enhancement [2], image abstraction [3] and segmentation [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Texture and Structure Aware Filtering Network for Image Smoothing

You

Barnes

2018

Lecture Notes in Computer Science

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Image smoothing is a fundamental task in computer vision, that aims to retain salient structures and remove insignificant textures. In this paper, we aim to address the fundamental shortcomings of existing image smoothing methods, which cannot properly distinguish textures and structures with similar low-level appearance. While deep learning approaches have started to explore the preservation of structure through image smoothing, existing work does not yet properly address textures. To this end, we generate a large dataset by blending natural textures with clean structure-only images, and then build a texture prediction network (TPN) that predicts the location and magnitude of textures. We then combine the TPN with a semantic structure prediction network (SPN) so that the final texture and structure aware filtering network (TSAFN) is able to identify the textures to remove ("texture-awareness") and the structures to preserve ("structure-awareness"). The proposed model is easy to understand and implement, and shows excellent performance on real images in the wild as well as our generated dataset.

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Section: Introductionmentioning

confidence: 99%

Deep Texture and Structure Aware Filtering Network for Image Smoothing

You

Barnes

2018

Lecture Notes in Computer Science

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“…He et al [15] presented a scheme of improvement on the RSF model in terms of robustness to initialization and noise. Wang et al [16] proposed a novel algorithm by using a piecewise smooth approximation to image. However, these models have some common drawbacks; in particular, they are not suitable to segment images with Poisson noise because these models are also formulated in view of the Mumford-Shah energy functional [2].…”

Section: Introductionmentioning

confidence: 99%

“…Following the RSF variational model, various extensions and analysis have been studied [7,[14][15][16], among others. In [7], Wang et al proposed a local and global intensity fitting model in a variational level set formulation.…”

Section: Introductionmentioning

confidence: 99%

Variational segmentation model for images with intensity inhomogeneity and Poisson noise

Chen

2013

J Image Video Proc

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In this paper, we propose a variational segmentation model to deal with intensity inhomogeneity and Poisson noise. An energy functional is first proposed, which uses a data-fidelity term deduced from Poisson distribution instead of the usual L 2 norm as a measure of fidelity. Due to the new data-fidelity measure, this energy functional can fit the image intensity more accurately while it can diminish the influence of Poisson noise on segmentation results. We then reformulate the energy function as globally convex formulation, which allows for more flexible initialization. The final convex energy functional is minimized via the dual formulation instead of the usually used gradient descent method. Experimental results show that the proposed model can efficiently segment images with intensity inhomogeneity and Poisson noise.

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“…After the Chan and Vese's work, different models based on the M-S functional with level set methods have been developed and widely adopted in various image applications [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Integrating clustering with level set method for piecewise constant Mumford-Shah model

Chen

2014

J Image Video Proc

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In the paper, we present an efficient method to solve the piecewise constant Mumford-Shah (M-S) model for two-phase image segmentation within the level set framework. A clustering algorithm is used to find approximately the intensity means of foreground and background in the image, and so the M-S functional is reduced to the functional of a single variable (level set function), which avoids using complicated alternating optimization to minimize the reduced M-S functional. Experimental results demonstrated some advantages of the proposed method over the well-known Chan-Vese method using alternating optimization, such as robustness to the locations of initial contour and the high computation efficiency.

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Image segmentation algorithm by piecewise smooth approximation

Cited by 9 publications

References 29 publications

Deep Texture and Structure Aware Filtering Network for Image Smoothing

Deep Texture and Structure Aware Filtering Network for Image Smoothing

Variational segmentation model for images with intensity inhomogeneity and Poisson noise

Integrating clustering with level set method for piecewise constant Mumford-Shah model

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