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2 results on '"statistical modeling of wavelet coefficients"'

1. De-noising of ultrasound image using Bayesian approached heavy-tailed Cauchy distribution.

Author

Sahu, Sima, Singh, Harsh Vikram, Kumar, Basant, and Singh, Amit Kumar

Subjects
PROBABILITY density function, SIGNAL processing, ULTRASONIC imaging, WAVELET transforms, IMAGE denoising
Abstract

Medical ultrasound images are used in clinical diagnosis and generally degraded by speckle noise. This makes difficulty in automatic interpretation of diseases in ultrasound images. This paper presents a speckle removal algorithm by modeling the wavelet coefficients. A Bayesian approach is implemented to find the noise free coefficients. Cauchy prior and Gaussian Probability Density Function (PDF) are used to model the true wavelet coefficients and noisy coefficients respectively. A Maximum a Posteriori (MAP) estimator is used to estimate the noise free wavelet coefficients. A Median Absolute Deviation (MAD) estimator is used to find the variance of affected wavelet coefficients in finest scale. The proposed method is compared with existing denoising methods. The experimental results show that the method offer up to 21.48% enhancement in Peak Signal to Noise Ratio (PSNR), 1.82% enhancement in Structural Similarity Index (SSIM), 1% enhancement in Correlation coefficient (ρ) and 7.68% enhancement in Edge Preserving Index (EPI) than best existing wavelet modeling method. The results indicate that the proposed method outperforms over existing methods, both in noise reduction and edge preservation. [ABSTRACT FROM AUTHOR]

Published
2019
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2. Interband structure modeling for oversampled multiresolution analysis-based Pan-sharpening of very high resolution multispectral images

Author

Stefano Baronti, Bruno Aiazzi, Andrea Garzelli, F. Nencini, and Luciano Alparone

Subjects
multiresolution analysis (MRA), Multiresolution analysis, Multispectral image, Sharpening, Wavelet, urban remote sensing, band sharpening, QuickBird, Computer vision, ARSIS, a-trous wavelet transform (ATWT), band sharpening, data fusion, interband structure modeling (IBSM), multiresolution analysis (MRA), QuickBird, statistical modeling of wavelet coefficients, urban remote sensing, very high resolution multispectral imagery, a-trous wavelet transform (ATWT), data fusion, Pixel, very high resolution multispectral imagery, business.industry, Pattern recognition, ARSIS, Sensor fusion, "a trous" wavelet transform (ATWT), Panchromatic film, statistical modeling of wavelet coefficients, Geography, Artificial intelligence, High-pass filter, business, interband structure modeling (IBSM)
Abstract

This paper addresses the modeling of wavelet coefficients for multispectral (MS) band sharpening based on undecimated multiresolution analysis (MRA). The coarse MS bands are sharpened by injecting highpass details taken from a high resolution panchromatic (Pan) image. Besides the MRA, crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. The goal is that the merged MS images are most similar to what the MS sensor would collect if it had the same resolution as the broadband Pan imager. Three injection models embedded in an "a trous" wavelet decomposition will be described and compared on a test set of very high resolution QuickBird MS+Pan data. Two models work on approximation and detail coefficients, respectively, and provide a certain degree of unmixing of coarse MS pixels. The third model is based on spectral fidelity of the merged image to the (resampled) original MS data, that is, no unmixing is attempted. Fusion comparisons on spatially degraded data, of which higher-resolution true MS data are available for reference, show that the former two models yield better results than the latter, in terms of both radiometric and spectral fidelity. The latter, however, yields a reliable sharpening unaffected by local artifacts, regardless of landscape complexity. When local statistics of wavelet coefficients are utilized, the model estimated on approximation yields slightly better but considerably stabler results than that calculated starting from bandpass details.

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