scholarly journals Polarized-state-based coding strategy and phase image estimation method for robust 3D measurement

2020 ◽  
Vol 28 (3) ◽  
pp. 4307
Author(s):  
Zhenmin Zhu ◽  
Yulin Xie ◽  
Yigang Cen
Keyword(s):  
Estimation Method ◽  
Phase Image ◽  
3D Measurement ◽  
Image Estimation ◽  
Coding Strategy

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

TH-CD-303-06: Deformable Registration-Based Image Estimation Method for 4D CBCT Using Region-Based PCA

2015 ◽  
Vol 42 (6Part43) ◽  
pp. 3729-3730
Author(s):  
W Harris ◽  
Y Zhang ◽  
F Yin ◽  
L Ren
Keyword(s):  
Estimation Method ◽  
Deformable Registration ◽  
Image Estimation

3D measurement and camera attitude estimation method based on trifocal tensor

2016 ◽  
Author(s):  
Shengyi Chen ◽  
Haibo Liu ◽  
Linshen Yao ◽  
Qifeng Yu
Keyword(s):  
Estimation Method ◽  
Attitude Estimation ◽  
3D Measurement ◽  
Trifocal Tensor

56.4: A Head-Up Display Illuminator Design and Virtual-Image Estimation Method

2014 ◽  
Vol 45 (1) ◽  
pp. 822-825 ◽  
Author(s):  
Tzu-Ling Niu ◽  
Yu-Chan Hsieh ◽  
Super Liao ◽  
Han-Ping Kuo
Keyword(s):  
Estimation Method ◽  
Virtual Image ◽  
Image Estimation

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

Interferometric Phase Image Estimation via Sparse Coding in the Complex Domain

2015 ◽  
Vol 53 (5) ◽  
pp. 2587-2602 ◽  
Author(s):  
Hao Hongxing ◽  
Jose M. Bioucas-Dias ◽  
Vladimir Katkovnik
Keyword(s):  
Sparse Coding ◽  
Complex Domain ◽  
Phase Image ◽  
Image Estimation ◽  
Interferometric Phase

scholarly journals Payload location for JPEG image steganography based on co-frequency sub-image filtering

2020 ◽  
Vol 16 (1) ◽  
pp. 155014771989956 ◽  
Author(s):  
Jie Wang ◽  
Chunfang Yang ◽  
Ping Wang ◽  
Xiaofeng Song ◽  
Jicang Lu
Keyword(s):  
Discrete Cosine Transform ◽  
Estimation Method ◽  
Image Filtering ◽  
Image Steganography ◽  
Experimental Results ◽  
Jpeg Image ◽  
Image Estimation ◽  
Location Method ◽  
Transform Coefficients ◽  
The Mean

In digital steganography, due to difficulties estimating the JPEG cover image, it is still very hard to accurately locate the hidden message embedded in a JPEG image. Therefore, this study proposes a payload location method for a category of pseudo-random scrambled JPEG image steganography. In order to estimate the quantized discrete cosine transform coefficients in the cover JPEG image, a cover JPEG image estimation method is proposed based on co-frequency sub-image filtering. The proposed payload location method defines a general residual, uses the estimated cover JPEG image to compute the residuals, and then employs the mean residuals of multiple stego images embedded along the same path to distinguish the stego positions. The proposed cover JPEG image estimation method constructs 64 co-frequency sub-images, and then filters the sub-image to estimate the cover JPEG image. Finally, using these methods, payload location algorithms are designed for two common JPEG image steganography algorithms: JSteg and F5. Experimental results show that the proposed location algorithms can effectively locate the stego positions in both JSteg and F5 steganography when the investigator possesses multiple stego images embedded along the same path. In addition, the location results can also be used to recover the steganography key to extract the embedded secret messages.

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