Dr. Jun Tian Profile

Dr. Jun Tian

R&D Engineer at Digimarc Corp

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Publications (7)

Proceedings Article | 29 April 2002 Paper

Wavelet-based reversible watermarking for authentication

Jun Tian

Proceedings Volume 4675, (2002) https://doi.org/10.1117/12.465329

KEYWORDS: Digital watermarking, Wavelet transforms, Binary data, Digital imaging, Wavelets, Image compression, Image retrieval, Image restoration, Sensors, Image quality

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Proceedings Article | 26 September 2001 Paper

Wavelet-based image compression and content authentication

Jun Tian

Proceedings Volume 4551, (2001) https://doi.org/10.1117/12.442887

KEYWORDS: Image compression, Digital watermarking, Digital imaging, Image quality, Modulation, Visualization, Visual process modeling, Visual compression, Image restoration, Binary data

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Proceedings Article | 24 August 2000 Paper

Automatic target recognition (ATR) performance on wavelet-compressed synthetic aperture radar (SAR) imagery

Michael Hoffelder, Jun Tian

Proceedings Volume 4053, (2000) https://doi.org/10.1117/12.396380

KEYWORDS: Synthetic aperture radar, Automatic target recognition, Image compression, Wavelets, Chromium, Detection and tracking algorithms, Visualization, Data storage, Target recognition, Target detection

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With the large amount of image data that can be produced in real-time by new synthetic aperture radar (SAR) platforms, such as Global Hawk, compression techniques will be needed for both transmission and storage of this data. Also to keep image analysts (IA's) from being overwhelmed, high-speed automatic target cueing and/or recognition (ATC, ATR) systems will be needed to help exploit this large amount of data in real-time. Past SAR image compression studies have used subjective visual ratings and/or statistical measures such as mean-squared-error (MSE) to compare compression performance. Statistical metrics are much more appealing than unreproducible biased visual interpretations. However, the use of statistical metrics, such as MSE, has practical limitations on SAR imagery due to the high frequency speckle noise that is characteristic. In this case, the MSE metric is dominated by how well the noise speckle is preserved -- a statistic that is of no consequence. Since the large amount of data that dictates the need for compression also dictates the need for ATR, a meaningful statistic would be ATR performance. This ATR performance metric would emphasize how well pixels on target are preserved. Therefore, we have investigated ATR performance using a wavelet compression technique, since this technique has achieved very high compression on other types of imagery. We have used the Rice University Computational Mathematics Laboratory's wavelet compression algorithm in conjunction with a 'synthetic discriminant function' (SDF) based ATR algorithm. The SDF technique was developed at Carnegie Mellon University and successfully applied to SAR imagery by the Northrop Grumman Science & Technology Center. This combination allows ATR performance to be parameterized as a function of compression rate. The SAR data used for this research was taken from the public-released MSTAR target and clutter data set. We show results for both target detection and target identification versus false alarms for varying compression rates.

Proceedings Article | 26 October 1999 Paper

Scale-band-dependent thresholding for signal denoising using undecimated discrete wavelet packet transforms

Huipin Zhang, Aria Nosratinia, C. Sidney Burrus, Jun Tian, Raymond Wells

Proceedings Volume 3813, (1999) https://doi.org/10.1117/12.366805

KEYWORDS: Wavelets, Signal to noise ratio, Transform theory, Denoising, Monte Carlo methods, Interference (communication), Discrete wavelet transforms, Wavelet transforms, Reconstruction algorithms, Electronic filtering

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Proceedings Article | 26 October 1999 Paper

CFAR-enhanced image codec for SONAR ATR

Jun Tian, Raymond Wells

Proceedings Volume 3813, (1999) https://doi.org/10.1117/12.366848

KEYWORDS: Image compression, Automatic target recognition, Target detection, Wavelet transforms, Wavelets, Detection and tracking algorithms, Sensors, Image fusion, Binary data, Image enhancement

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Proceedings Article | 26 March 1998 Paper

Fast implementation of wavelet transform for m-band filter banks

Jun Tian, Raymond Wells

Proceedings Volume 3391, (1998) https://doi.org/10.1117/12.304902

KEYWORDS: Wavelets, Discrete wavelet transforms, Filtering (signal processing), Matrices, Radon, Wavelet transforms, Adaptive optics, Fast wavelet transforms, Computational mathematics, Signal processing

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Proceedings Article | 10 June 1996 Paper

Evaluation of a new wavelet-based compression algorithm for synthetic aperture radar images

Jun Tian, Haitao Guo, Raymond Wells, C. Sidney Burrus, Jan Odegard

Proceedings Volume 2757, (1996) https://doi.org/10.1117/12.242055

KEYWORDS: Image compression, Synthetic aperture radar, Wavelets, Discrete wavelet transforms, Image processing, Wavelet transforms, Binary data, Data processing, Algorithms, Data compression

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Showing 5 of 7 publications

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