&lt;title&gt;Performance of nonlinear methods in medical image restoration&lt;/title&gt;

Castellanos, Ramiro; Castillo, Hiedra; Mitra, Sunanda

doi:10.1117/12.341091

Cited by 9 publications

(2 citation statements)

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“…In the second step, fuzzy C-means 13 (FCM) optimization is used to update the centroids and the fuzzy membership values of the samples. AFLC has been tested and used for image segmentation, restoration, and compression as well as other pattern recognition tasks 4,14,15 . It has been consistently shown to be a very effective clustering algorithm for its good performance and fast execution.…”

Section: Vector Quantizationmentioning

confidence: 99%

<title>Efficient color image reconstruction by color multiplexing and vector quantization in the wavelet domain</title>

Yang

Mitra

2001

SPIE Proceedings

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Color images are usually represented by three-color planes. For high fidelity low bit rate image storage or transmission, redundancy of information on theses color planes can be further reduced when combined with other image coding techniques. Instead of generating three codebooks for three color planes individually when vector quantization (VQ) is applied, as is regularly done in color image compression with VQ, our new approach is to reduce three color planes into one multiplexed plane using a spatial color-multiplexing technique 1 , achieving a 3:1 compression before quantization. Wavelet transform is then applied on the multiplexed plane. Vector quantization of the wavelet coefficients is performed by using an adaptive fuzzy leader clustering 2 (AFLC) approach. Inverse wavelet transform and demultiplexing at the decoder side are required to recover the color image in the spatial domain. Our experiment shows that this new scheme yields high fidelity reconstruction at a considerably lower bit rate than the bit rate achievable without color multiplexing.

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Section: Vector Quantizationmentioning

confidence: 99%

<title>Efficient color image reconstruction by color multiplexing and vector quantization in the wavelet domain</title>

Yang

Mitra

2001

SPIE Proceedings

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“…Image restoration is a common task in image processing with lots of real-world applications such as astronomical imaging [1], [2], medical imaging [3], [4] and so on. Generally speaking, the image degradation usually comes from the imaging process, e.g., the motion of imaging objects, sensing device and propagation perturbation of the atmosphere between the objects and sensing device [5].…”

Section: Introductionmentioning

confidence: 99%

Model identification and parameters estimation for image deblurring

Gao¹,

Wang²,

Zhu³

2014

2014 International Conference on Information and Communications Technologies (ICT 2014)

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Blurred images usually come from all kinds of imaging equipment, which may provide important prior information about the modulation transfer function (MTF), i.e., the frequency representation of the blur, such as the possible MTF models with unknown parameters. Based on this prior, we aim to identify the actual degradation MTF models and estimate their corresponding parameters for image deblurring. We firstly analyse the statistical character of the blurred image from the fractal model; and then estimate the noise level by the high frequency energy of the blurred image. For given MTF degradation model, we estimate the fractal parameters and the MTF parameters by Maximum Likelihood Estimation (MLE), which is achieved numerically by alternating optimization scheme. For unknown MTF models, we propose likelihood ratio test and joint parameter estimation for the identification of the single MTF model and multi-MTF models, respectively. Therefore, the degradation MTF models can be obtained before the image deconvolution, which means that the proposed method translates the blind image deconvolution to the non-blind one. The numerical experiments test the accuracy of the parameter estimation and the correctness of the MTF model identification.

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Statistical and Adaptive Approaches for Optimal Segmentation in Medical Images

Yang

Mitra

Topics in Biomedical Engineering International Book Series

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<title>Performance of nonlinear methods in medical image restoration</title>

Cited by 9 publications

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<title>Efficient color image reconstruction by color multiplexing and vector quantization in the wavelet domain</title>

<title>Efficient color image reconstruction by color multiplexing and vector quantization in the wavelet domain</title>

Model identification and parameters estimation for image deblurring

Statistical and Adaptive Approaches for Optimal Segmentation in Medical Images

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