An evaluation of SAR image compression techniques

Sakarya, F.A.; Dong, Wei; Emek, S.

doi:10.1109/icassp.1997.595379

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…4(c)-4(f), it is observed that the result of the proposed method is better than others. To measure the performance quantitatively, the peak signal-to-noise ratio (PSNR) 22 is calculated, which is defined as PSNR ¼ 10 log 10 ðMAX − MINÞ 2 MSE ;…”

Section: Resultsmentioning

confidence: 99%

Universal deblurring method for real images using transition region

2012

Opt. Eng

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In this paper, we present a universal deblurring method for real images without prior knowledge of the blur source. The proposed method uses the transition region of the blurred image to estimate the point spread function (PSF). It determines the main edges of the blurred image with high edge measures based on the difference of Gaussians (DoG) operator. Those edge measures are used to predict the transition region of the sharp image. By using the transition region, we select the pixels of the blurred image to form a series of equations for calculating the PSF. In order to overcome noise disturbance, the optimal method based on the anisotropic adaptive regularization is used to estimate the PSF, in which the constraints of non-negative and spatial correlations are incorporated. Once the PSF is estimated, the blurred image is effectively recovered by employing nonblind restoration. Experimental results show that the proposed method performs effectively for real images with different blur sources.

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Section: Resultsmentioning

confidence: 99%

Universal deblurring method for real images using transition region

2012

Opt. Eng

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“…The purpose is to compress the data via some transformation, which leaves the data and noise separate, and inverse transform only the section corresponding to the data to remove the noise. These techniques have been primarily explored via the wavelet transform, the cosine transform, and the Karhunen-Loeve transform [25][26][27][28][29] .…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient computation of superresolution methods for SAR imaging

Batts,

Rigling

2023

Algorithms for Synthetic Aperture Radar Imagery XXX

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While traditional Fourier methods of SAR imaging are well known in addition to being easy to implement, they have limitations in terms of quality, particularly with respect to speckle, scintillation, and side lobe artifacts. Methods of SAR imaging that have shown promise include superresolution methods like the Minimum Variance Method (MVM) and the Multiple Signal Classification (MUSIC) algorithm; however, these algorithms are computationally intense. Both algorithms require the estimation of a correlation matrix, and manipulations thereof, as well as computing the image spectrum through computation of a quadratic form for each image pixel. This paper presents an efficient method for estimating the correlation matrix and shows how the structure of the correlation matrix can be exploited to efficiently compute the aforementioned superresolution methods.

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