Total Variation Denoising and Enhancement of Color Images Based on the CB and HSV Color Models

Chan, Tony F.; Kang, Sung Ha; Shen, Jianhong

doi:10.1006/jvci.2001.0491

Cited by 152 publications

(116 citation statements)

References 15 publications

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“…Pekel et al [30] used HSV transformation of mid-near infrared, near infrared, and red bands of MODIS multi-spectral time series data for developing automated algorithms for near real-time water surface detection, and characterization of their spatial and temporal dynamics. Several researchers have reported better target identification and extraction by the HSV (Hue, Saturation, and Value) color model than by the RGB model [30][31][32]. Verpoorter et al [33] produced high-resolution global database of lakes using Landsat 7 data.…”

Section: Introductionmentioning

confidence: 99%

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

et al. 2015

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Abstract:Monitoring of water cover and shorelines at a global scale is essential for better understanding climate change consequences and modern human disturbances. The level and turbidity of the surface water, and the background objects in which they interact with, vary significantly at a global scale. The existing water indices applicable to detection and extraction of water cover at local and regional scales cannot work efficiently everywhere in the globe. In this research, a new water index called Superfine Water Index (SWI) was developed for robust detection and discrimination of the surface water at a global scale using MODIS based multispectral data. The SWI was designed in such a way that it provides high contrast between the water and non-water areas. Achieving high contrast is vital for discriminating the surface water mixed with a variety of objects. The sensitivity analysis of the SWI demonstrated its high sensitivity to the surface water compared to the existing water indices. One single-layered global mosaic of a 90-percentile SWI image was used as a master image for global water cover mapping by reducing the large volume of MODIS data available between 2012 and 2014 globally. The random walker algorithm was applied in the SWI image with the support of reference training data for the extraction and mapping of water cover. This research produced an up-to-date global water cover map of the year 2013. OPEN ACCESSRemote Sens. 2015, 7 13808The performance of a new map was evaluated with a number of case studies and compared with existing maps. The supremacy of the SWI over the existing water indices, and high performance of the SWI based water map confirmed the reliability of the new water mapping methodology developed. We expect that this methodology can contribute to seasonal and annual change analysis of the global water cover as well.

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Section: Introductionmentioning

confidence: 99%

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

et al. 2015

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“…Since the pioneering work of Rudin et al [1], TV [1] and vectorial TV models [2][3][4][5] have been demonstrated very successful in image restoration. The success of TV and vectorial TV relies on its good edge preserving property, which suits most images where the edges are sparse.…”

Section: Introductionmentioning

confidence: 99%

Augmented Lagrangian Method for Total Variation Based Image Restoration and Segmentation Over Triangulated Surfaces

Wu¹,

Zhang

Duan

et al. 2011

J Sci Comput

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Recently total variation (TV) regularization has been proven very successful in image restoration and segmentation. In image restoration, TV based models offer a good edge preservation property. In image segmentation, TV (or vectorial TV) helps to obtain convex formulations of the problems and thus provides global minimizations. Due to these advantages, TV based models have been extended to image restoration and data segmentation on manifolds. However, TV based restoration and segmentation models are difficult to solve, due to the nonlinearity and non-differentiability of the TV term. Inspired by the success of operator splitting and the augmented Lagrangian method (ALM) in 2D planar image processing, we extend the method to TV and vectorial TV based image restoration and segmentation on triangulated surfaces, which are widely used in computer graphics and computer vision. In particular, we will focus on the following problems. First, several Hilbert spaces will be given to describe TV and vectorial TV based variational models in the discrete setting. Second, we present ALM applied to TV and vectorial TV image restoration on mesh surfaces, leading to efficient algorithms for both gray and color image restoration. Third, we discuss ALM for vectorial TV based multi-region image segmentation, which also works for both gray and color images. The proposed method benefits from fast solvers for

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“…As a consequence, if the data fidelity term does not introduce a coupling either, the minimization of an energy of type (1) amounts to a series of independent scalar TV minimization problems, which gives visual artifacts in image processing (see [19,28]). …”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Textures: Application to Color Image Decomposition with a Projected Gradient Algorithm

Duval

Aujol

Vese³

2010

J Math Imaging Vis

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In this paper, we are interested in texture modeling with functional analysis spaces. We focus on the case of color image processing, and in particular color image decomposition. The problem of image decomposition consists in splitting an original image f into two components u and v. u should contain the geometric information of the original image, while v should be made of the oscillating patterns of f , such as textures. We propose here a scheme based on a projected gradient algorithm to compute the solution of various decomposition models for color images or vector-valued images. We provide a direct convergence proof of the scheme, and we give some analysis on color texture modeling.

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Total Variation Denoising and Enhancement of Color Images Based on the CB and HSV Color Models

Cited by 152 publications

References 15 publications

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

Augmented Lagrangian Method for Total Variation Based Image Restoration and Segmentation Over Triangulated Surfaces

Mathematical Modeling of Textures: Application to Color Image Decomposition with a Projected Gradient Algorithm

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