Signal synthesis and reconstruction from partial Fourier-domain information

Oppenheim, Alan V.; Lim, Jae S.; Curtis, Susan R.

doi:10.1364/josa.73.001413

Cited by 71 publications

(40 citation statements)

References 7 publications

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“…both experimentally and theoretically [1]. On the experimental side, we have shown with images that if the correct one bit of phase is combined with a unity or average magnitude, the resulting image maintains many of the features of the original image, and in fact, is identical to the phase-only version of the even (symmetric) component of the image.…”

Section: Abstract Continue On Reerse If Neceuar' and Identtfy By Blocmentioning

confidence: 88%

“…Theorem 5 is related to a result developed in [1] on the unique specification of one-dimensional signals with Fourier transform sign information. The one-dimensional result states constraints on the zeros of the z-transform X (z) which guarantee that X,(z) has zeros only on the unit circle (and that these zeros are simple zeros) and thus that sign {X, (w)} is sufficient to uniquely specify x [n ].…”

Section: Extensionsmentioning

confidence: 99%

“…We have also previously established conditions under which a one-dimensional signal is uniquely specified to within a scale factor with Fourier transform sign information [1], although these conditions are fairly restrictive. One-dimensional signals are uniquely specified with this information only if the real part of the Fourier transform contains a sufficient number of sign changes, or zero crossings.…”

Section: Abstract Continue On Reerse If Neceuar' and Identtfy By Blocmentioning

confidence: 99%

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Signal reconstruction from Fourier transform sign information

Curtis

Oppenheim

Lim

1985

IEEE Trans. Acoust., Speech, Signal Process.

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Section: Abstract Continue On Reerse If Neceuar' and Identtfy By Blocmentioning

confidence: 88%

Section: Extensionsmentioning

confidence: 99%

Section: Abstract Continue On Reerse If Neceuar' and Identtfy By Blocmentioning

confidence: 99%

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Signal reconstruction from Fourier transform sign information

Curtis

Oppenheim

Lim

1985

IEEE Trans. Acoust., Speech, Signal Process.

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“…It has been experimentally established that for many images, the phase of the Fourier transform is more important than the magnitude [15], [24], [25]. Specifically if…”

Section: Significance Of Magnitude and Phasementioning

confidence: 99%

Lossless Quality Steganographic Color Image Compression

Rabie¹

2015

ijacsa

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Abstract-This paper develops a steganography-based paradigm for lossless-quality compression of high-resolution color images acquired by megapixel cameras. Our scheme combines space-domain and frequency-domain image processing operations where in the space domain, color-brightness separation is exploited, and in the frequency domain, spectral properties of the Fourier magnitude and phase of the color image is exploited. Working in both domains concurrently allows for an approach to ultrahigh-resolution image compression that addresses both issues of quality and storage size. Experimental results as well as empirical observations show that our technique exceeds the highest quality JPEG image compression standard in terms of compression rates while being very competitive with JPEG in the overall fidelity of the decompressed image, with the added advantage of being able to recover the original fine details in the color image without any degradations common in lossy image compression techniques.

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“…In most previous research pertaining to PSM, researchers have tried to use the rich information contained in the Fourier transform phase [8,9,15,30]. This is mainly attributed to successes in the study of image reconstruction from partial Fourier transform information, where the Fourier phase has been shown to contain more information than the Fourier magnitude [6,11,21,22]. So far, however, all results in texture analysis research seem to show that the textural content of the phase information is low.…”

Section: Fourier Transform Featuresmentioning

confidence: 99%

Optical and Sonar Image Classification: Wavelet Packet Transform vs Fourier Transform

Tang

Stewart

2000

Computer Vision and Image Understanding

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To develop a noise-insensitive texture classification algorithm for both optical and underwater sidescan sonar images, we study the multichannel texture classification algorithm that uses the wavelet packet transform and Fourier transform. The approach uses a multilevel dominant eigenvector estimation algorithm and statistical distance measures to combine and select frequency channel features of greater discriminatory power. Consistently better performance of the higher level wavelet packet decompositions over those of lower levels suggests that the Fourier transform features, which may be considered as one of the highest possible levels of multichannel decomposition, may contain more texture information for classification than the wavelet transform features. Classification performance comparisons using a set of sixteen Vistex texture images with several level of white noise added and two sets of sidescan sonar images support this conclusion. The new dominant Fourier transform features are also shown to perform much better than the traditional power spectrum method.

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Signal synthesis and reconstruction from partial Fourier-domain information

Cited by 71 publications

References 7 publications

Signal reconstruction from Fourier transform sign information

Signal reconstruction from Fourier transform sign information

Lossless Quality Steganographic Color Image Compression

Optical and Sonar Image Classification: Wavelet Packet Transform vs Fourier Transform

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