Polar Linear Canonical Transform in Quaternion Domain

Nowadays, almost all images acquired are in color format. Traditional methods process color images by either transforming them into gray scale or dividing them into red, green, and blue components for independent processing, which is definitely not effective in representing color information. Recently, a novel Polar Linear Canonical Transform (PLCT) with parameters in SL(2,<) has been reported, which is a generalization of the well-known Polar Harmonic Transform (PHT). However, PLCT is defined on gray-scale images, so it cannot handle color images directly. To solve the problem, this paper generalizes PLCT from complex domain to hypercomplex domain using quaternion algebras, producing the Quaternion Polar Linear Canonical Transform (QPLCT). The performance of QPLCT is then evaluated with Quaternion Fractional Polar Exponential Transform (QPFrET) as an example. The experimental results show that the QPLCT performs better than the commonly used Quaternion form Zernike Moment (QZM) and pseudo-Zernike Moment (QPZM) in terms of image representation capability and numerical stability.

[1]  Xiao-Ping Zhang,et al.  Color Image Watermarking Using Multidimensional Fourier Transforms , 2008, IEEE Transactions on Information Forensics and Security.

[2]  John F. Roddick,et al.  No-reference Quality Metric of Blocking Artifacts Based on Orthogonal Moments , 2014, J. Inf. Hiding Multim. Signal Process..

[3]  Yue-Nan Li Quaternion Polar Harmonic Transforms for Color Images , 2013, IEEE Signal Processing Letters.

[4]  Gang Chen,et al.  Color Image Analysis by Quaternion-Type Moments , 2014, Journal of Mathematical Imaging and Vision.

[5]  S. Sangwine Fourier transforms of colour images using quaternion or hypercomplex, numbers , 1996 .

[6]  A. S. Solodovnikov,et al.  Hypercomplex Numbers: An Elementary Introduction to Algebras , 1989 .

[7]  Jiasong Wu,et al.  Quaternion Bessel-Fourier moments and their invariant descriptors for object reconstruction and recognition , 2014, Pattern Recognit..

[8]  Xudong Jiang,et al.  Application of Polar Harmonic Transforms to Fingerprint Classication , 2011 .

[9]  Huazhong Shu,et al.  Robust hashing for image authentication using quaternion discrete Fourier transform and log-polar transform , 2015, Digit. Signal Process..

[10]  Jeng-Shyang Pan,et al.  Geometrically invariant image watermarking using Polar Harmonic Transforms , 2012, Inf. Sci..

[11]  Ghazali Sulong,et al.  A Novel Approach for Detection of Copy Move Forgery using Completed Robust Local Binary Pattern , 2015, J. Inf. Hiding Multim. Signal Process..

[12]  Xudong Jiang,et al.  Two-Dimensional Polar Harmonic Transforms for Invariant Image Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Gang Chen,et al.  Quaternion Zernike moments and their invariants for color image analysis and object recognition , 2012, Signal Process..

[14]  Licheng Yu,et al.  Vector Sparse Representation of Color Image Using Quaternion Matrix Analysis , 2015, IEEE Transactions on Image Processing.

[15]  Huafei Sun,et al.  Image watermarking using polar harmonic transform with parameters in SL(2, r) , 2015, Signal Process. Image Commun..

[16]  Orly Yadid-Pecht,et al.  Quaternion Structural Similarity: A New Quality Index for Color Images , 2012, IEEE Transactions on Image Processing.

[17]  Li Li,et al.  Multiple Watermark Scheme based on DWT-DCT Quantization for Medical Images , 2015, J. Inf. Hiding Multim. Signal Process..

[18]  Huafei Sun,et al.  Image watermarking via fractional polar harmonic transforms , 2015, J. Electronic Imaging.

[19]  Eric C. Larson,et al.  Most apparent distortion: full-reference image quality assessment and the role of strategy , 2010, J. Electronic Imaging.

[20]  Ming Zhu,et al.  Quaternion Fourier-Mellin moments for color images , 2011, Pattern Recognit..