On texture and image interpolation using Markov models

Nemirovsky, Shira; Porat, Moshe

doi:10.1016/j.image.2008.11.001

Cited by 17 publications

(9 citation statements)

References 39 publications

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“…The first order wide-sense Markov model described above behaves quite well for small local regions and proved itself in the context of texture interpolation and reconstruction [11]. In this work we have used the three values of the auto and cross-correlation function within and between color bands.…”

Section: Feature Extractionmentioning

confidence: 98%

A New Approach to Texture Recognition Using Decorrelation Stretching

Kalfon¹,

Porat²

2013

IJFCC

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Abstract-Texture features have always been a key attribute in image recognition and classification. In this paper we propose two pre-processing methods for enhancing the performance of widely used color texture recognition methods. In the first approach we propose decorrelation stretching for color enhancement, which is known to improve the interpretability of color images. The second method employs Cartoon-Texture decomposition for sharpening the texture component of the image. We show that both methods improve the classification accuracy by 7% and 4% respectively when applied to images prior to extracting auto and cross-correlation features. Our conclusion is that the proposed a p p r o a c h could be helpful in machine vision tasks.

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Section: Feature Extractionmentioning

confidence: 98%

A New Approach to Texture Recognition Using Decorrelation Stretching

Kalfon¹,

Porat²

2013

IJFCC

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“…Non-Local WeightsIn [15], Buades et al used a non-local filter (NLM) for the purpose of image denoising. We use a similar expression as the weight that is assigned to each low resolution patch reflecting its similarity to the high resolution patch.…”

Section: A Interpolation Of the Intensity Component Localnon-local Bmentioning

confidence: 99%

“…Due to their importance, several approaches to texture analysis have been investigated over the years, such as statistical based techniques [12], techniques based on the Markov random field (MRF) model [13] and frequency domain techniques [14]. The MRF model has also been used for texture interpolation [15]. A different approach that has been used for texture filtering [16] and interpolation [10] is NLM.…”

Section: Introductionmentioning

confidence: 99%

A Single-Image Super-Resolution Method for Texture Interpolation

Kalit¹,

Porat²

2013

IJFCC

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Abstract-In recent years, a number of super-resolution techniques have been proposed. Most of these techniques construct a high resolution image by either combining several low resolution images at sub-pixel misalignments or by learning correspondences between low and high resolution image pairs. In this paper we present a stochastic super-resolution method for color textures from a single image. The proposed algorithm takes advantage of the repetitive nature of textures and the existence of several similar patches within the texture, as well as the color-intensity correlation that often exist in natural images. In the first step of the algorithm the intensity component is interpolated. For each pixel, the missing value is chosen according to a probability distribution constructed from a measure of similarity to other patches in the texture as well as from local features and patch color similarity. In the second stage, the color components are interpolated in a similar manner, using patches of the color channels as well as the already interpolated intensity values. Our conclusion is that the proposed approach outperforms presently available methods.

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“…Although continuous-time ARMA (CARMA) stochastic processes have been successfully exploited for image modeling [5,6], the continuous-domain approach has never been investigated in the processing of ultrasound images. In this study, we consider the radio-frequency (RF) ultrasound signal as a sampled CARMA process and recover its CARMA parameters from the traditional ARMA coefficients through an indirect approach.…”

Section: Introductionmentioning

confidence: 99%

Continuous-domain ARMA modeling for ultrasound tissue characterization

Maggio

Alessandrini

Speciale

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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In this study, we investigate the possibility of applying a continuous-time ARMA (CARMA) model to radio-frequency ultrasound signals. We consider the effect of the discretization process on the parameters of the continuous system, and we take into account the exponential nature of the autocorrelation function of the model to derive continuous-domain information from the parameters of the discrete ARMA process. We validate the effectiveness of the CARMA model parameters for the characterization of ultrasound tissues on a sequence of phantom images that represent various concentrations of scatterers. We also compare the proposed CARMA coefficients and the traditional ARMA parameters on the basis of their performance in discriminating between phantom tissues. We show that working in the continuous domain brings additional useful information to characterize the imaged materials.

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On texture and image interpolation using Markov models

Cited by 17 publications

References 39 publications

A New Approach to Texture Recognition Using Decorrelation Stretching

A New Approach to Texture Recognition Using Decorrelation Stretching

A Single-Image Super-Resolution Method for Texture Interpolation

Continuous-domain ARMA modeling for ultrasound tissue characterization

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