Adaptive smoothing via contextual and local discontinuities

Chen, Ke

doi:10.1109/tpami.2005.190

Cited by 102 publications

(37 citation statements)

References 49 publications

(147 reference statements)

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“…Chen [63] classified the existing performance evaluation methods into three categories; i.e. subjective, objective and application-based methodologies.…”

Section: Methodsmentioning

confidence: 99%

A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising

Wang

Niu

Zhang

et al. 2011

EURASIP J. Adv. Signal Process.

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This work presents a scale-based forward-and-backward diffusion (SFABD) scheme. The main idea of this scheme is to perform local adaptive diffusion using local scale information. To this end, we propose a diffusivity function based on the Minimum Reliable Scale (MRS) of Elder and Zucker (IEEE Trans. Pattern Anal. Mach. Intell. 20 (7), [699][700][701][702][703][704][705][706][707][708][709][710][711][712][713][714][715][716] 1998) to detect the details of local structures. The magnitude of the diffusion coefficient at each pixel is determined by taking into account the local property of the image through the scales. A scale-based variable weight is incorporated into the diffusivity function for balancing the forward and backward diffusion. Furthermore, as numerical scheme, we propose a modification of the Perona-Malik scheme (IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629-639, 1990) by incorporating edge orientations. The article describes the main principles of our method and illustrates image enhancement results on a set of standard images as well as simulated medical images, together with qualitative and quantitative comparisons with a variety of anisotropic diffusion schemes.

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“…Chen [63] classified the existing performance evaluation methods into three categories; i.e. subjective, objective and application-based methodologies.…”

Section: Methodsmentioning

confidence: 99%

A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising

Wang

Niu

Zhang

et al. 2011

EURASIP J. Adv. Signal Process.

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“…If k is chosen to be small, then the entire highlight region is preserved, and no smoothing is performed. According to K. Chen, when the value of k is defined as 7.5, the result shows better performance of contrast enhancement over the value of k [21]. To estimate local illumination the value of k is set as 7.5.…”

Section: Local Illumination and Reflectance Estimationmentioning

confidence: 99%

A Contrast Enhancement Method for HDR Image Using a Modified Image Formation Model

Yun

Hong

Choi

2012

IEICE Trans. Inf. & Syst.

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SUMMARYPoor illumination and viewing conditions have negativeinfluences on the quality of an image, especially the contrast of the dark and bright region. Thus, captured and displayed images usually need contrast enhancement. Histogram-based or gamma correction-based methods are generally utilized for this. However, these methods are global contrast enhancement method, and since the sensitivity of the human eye changes locally according to the position of the object and the illumination in the scene, the global contrast enhancement methods have a limit. The spatial adaptive method is needed to overcome these limitations and it has led to the development of an integrated surround retinex (ISR), and estimation of dominant chromaticity (EDC) methods. However, these methods are based on Gray-World Assumption, and they use a general image formation model, so the color constancy is known to get poor results, shown through graying-out, halo-artifacts (ringing effects), and the dominated color. This paper presents a contrast enhancement method using a modified image formation model in which the image is divided into three components: global illumination, local illumination and reflectance. After applying the power constant value to control the contrast in the resulting image, the output image is obtained from their product to avoid or minimize a color distortion, based on the sRGB color representation. The experimental results show that the proposed method yields better performances than conventional methods. key words: modified image formation model, global illumination, local illumination, reflectance, JND-based adaptive smoothing method

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“…Denoising, as the word suggests, is the removal of noisy components from the pixels of an image. Lots of research has been concentrated on this area for a long time, and many methodologies have been proposed by researchers for achieving good performance, [1][2][3][4][5][6] in which partial differential equation (PDE)-based image processing techniques offer great potential in developing image denoising applications with good results. However, these conventional PDE-based models might lose interesting fine structures during the denoising process.…”

Section: Introductionmentioning

confidence: 99%

Fractional domain varying-order differential denoising method

Zhang

et al. 2014

Opt. Eng

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Abstract. Removal of noise is an important step in the image restoration process, and it remains a challenging problem in image processing. Denoising is a process used to remove the noise from the corrupted image, while retaining the edges and other detailed features as much as possible. Recently, denoising in the fractional domain is a hot research topic. The fractional-order anisotropic diffusion method can bring a less blocky effect and preserve edges in image denoising, a method that has received much interest in the literature. Based on this method, we propose a new method for image denoising, in which fractional-varying-order differential, rather than constant-order differential, is used. The theoretical analysis and experimental results show that compared with the state-of-the-art fractional-order anisotropic diffusion method, the proposed fractional-varying-order differential denoising model can preserve structure and texture well, while quickly removing noise, and yields good visual effects and better peak signal-to-noise ratio.

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Adaptive smoothing via contextual and local discontinuities

Cited by 102 publications

References 49 publications

A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising

A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising

A Contrast Enhancement Method for HDR Image Using a Modified Image Formation Model

Fractional domain varying-order differential denoising method

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