Robust Sub-pixel Image Registration Based on ...

IEEE ISCE 2014 1569954047

Robust Sub-pixel Image Registration Based on Combination of Local Phase Correlation and Feature Analysis Vivek Maik Eunjung Chae1, Eunsung Lee1, Department of Electronics and Gwanghyun Jo1, Sunhee Park2, Jinhee Communication Han3, and Joonki Paik1,2 1 Department of Image The Oxford College of Engineering, 2 School of Integrative Engineering Bangalore, India 3 Department of Physics [email protected] Chung-Ang University, Seoul, Korea

Chanyong Park SIC R&D Laboratory LG Electronics Inc. Seoul, Korea [email protected]

[email protected], [email protected], [email protected], [email protected], [email protected], [email protected] translation is estimated to sub-pixel precision using local phase correlation. The registered image is then obtained by translation using the estimated sub-pixel shifting vector.

Abstract— This paper presents a robust sub-pixel image registration algorithm by analyzing intensity variation of local features. The proposed method consists of three steps: i) global image registration using features analysis, ii) local image registration using phase correlation, and iii) sub-pixel image translation. It can register two images without mis-registration artifacts because of the accurate estimation of inter-image transformation estimation by using local phase correlation and feature analysis. The proposed algorithm can be applied for various image processing systems including image registration, video stabilization, and multi-sensor image fusion.

II.

For the robust image registration to image rotation and scaling, the proposed registration method uses local phase correlation and feature analysis as shown Fig. 1. After detecting features using Harris corner detector, the rotation angle and scaling factor is simply computed as [3]

Keywords— sub-pixel; image registration; phase correlation; feature analysis

I.

IMAGE REGISTRATION BASED ON LOCAL PHASE CORRELATION AND FEATURE ANALYSIS

ª f2 C = ¦¦ w(u , v) « x u v «¬ f x f y

INTRODUCTION

Image registration is to align multiple images acquired by different sensors, from different viewpoints, and in different times. Recently, image registration has been used in various imaging applications such as object recognition, stereo vision, video stabilization, and 3D reconstruction.

fx f y º », f y2 »¼

(1)

where w(u, v) represents the window function, f the input image, f x the gradient along the x -axis, and f y the gradient along the y -axis. If two eigenvalues of C are larger than a pre-specified threshold, the region is determined as corner.

Over the past few decades, many sub-pixel image registration algorithms have been proposed. Simple, basic registration algorithms are based on image interpolation, which results in blurring artifacts and registration error when images are relatively rotated or scaled [1]. Balci proposed a sub-pixel image registration algorithm using the relationship between the continuous and discrete phase difference [2]. Balci’s method estimates the number of cycles of the phase difference matrix and computes accurate registration parameters. However, it still cannot register the images with rotation and/or scales. For aligning a pair of images to sub-pixel precision, a robust sub-pixel image registration algorithm is presented using combined local phase correlation and feature analysis. The proposed algorithm first extracts feature points using Harris corner detection, and performs image warping to deal with inter-image rotation and scaling. Next, the amount of

Fig. 1. Block diagram of the proposed sub-pixel image registration algorithm.

1

TABLE I.

We perform image warping in the target image by estimating the geometric transformation matrix which is determined from the estimated feature points. However, the warped image still has unregistered regions because of the estimation error of feature extraction and local motion. Therefore, we compute the local phase correlation as

Pi (u , v) =

ℑ{ f i ,tar ( x, y )}ℑ{ fi ,ref ( x, y )}* | ℑ{ fi ,tar ( x, y}ℑ{ fi ,ref ( x, y )}* }

,

Rotation Angle 5

(2) 10

where ( x, y ) represents the image coordinate, ℑ the Fourier transform operator, the superscript ‘ * ’ the complex conjugate operator, f i ,ref the i -th block in the reference image, f i ,tar the i -th block in the target image. The local pixel shifting vector (dxi , dyi ) is computed pi ( x, y ) = ℑ−1{Pi (u, v)},

To estimate accurate sub-pixel shift values, we minimize the energy function given in [2], and the resulting shift values are computed as pi (dxi + 1, dyi ) , pi (dxi + 1, dyi ) ± pi ( dxi , dyi )

Δyi =

pi (dxi , dyi + 1) . pi (dxi , dyi + 1) ± pi (dxi , dyi )

(4)

This work was supported by the National Research Foundation of KOREA (NRF) and Center for Women In Science, Engineering and Technology (WISET) Grant funded by the Ministry of Science, ICT & Future Planning of KOREA (MSIP) under the program for the Potential female students with interest in Science connecting with the community of Science and Engineering, by LG Electronics Inc., and by the Ministry of Science, ICT & Future Planning as Software Grand Challenge Project (grant no. 14-824-09-003).

EXPERIMENTAL RESULTS

For evaluating the performance of the proposed registration algorithm, the Lena image is transformed by different shifting values and rotation angles. Experimental results of the registration are shown in Fig. 2. The result of Balci’s method shows color disparity in the unregistered edge regions. However, the propose algorithm successfully register the transformed image.

REFERENCES [1] [2]

[3]

(a)

(b)

(c)

Proposed Method 0.0851 0.0853 0.0856 0.0857 0.1147 0.1135 0.1136 0.1189

ACKNOWLEDGMENT

The finally registered image is obtained by performing local image shifting according to the sub-pixel shift values. III.

Balci’s Method 0.1761 0.1775 0.1931 0.2038 0.3699 0.3335 0.3322 0.3488

IV. CONCLUSION In this paper, we presented a sub-pixel precision image registration algorithm by using local phase correlation and feature analysis. Since existing registration methods do not deal with rotation and scaling, the registration performance is limited. The proposed algorithm can efficiently align a pair of images since it takes rotation and scaling into account as well as translation. Experimental results showed that the proposed image registration algorithm can be used for video stabilization, medical image processing and multi-sensor image registration.

x, y

Δxi =

Shift value 0.1 0.2 0.5 0.7 0.1 0.2 0.5 0.7

Table I summarizes root mean squared error (RMSE) values between the reference and registered images. The proposed algorithm provides lower RMSE values than Balci’s method.

(3)

[dxi ,dyi ] = arg max pi ( x, y ),

RMSE VALUES OF DIFFERENT ALGORITHM

(d)

Fig. 2. Comparison of different registration algorithm; (a) the reference image, (b) the transformed image (shift value: 0.1, rotation angle: 5°), (c) the registered image by the Balci’s method [2], and (d) the proposed result.

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L. G. Brown, “A Survey of image registration techniques,” ACM Computing Surveys, vol. 24, no. 4, pp. 325-376, December 1992. M. Balci and H. Foroosh, “Subpixel estimation of shifts directly in the Fourier domain,” IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 1965-1972, July 2006. Y. Qiao, Y. Tang, and J. Li, “Improved Harris sub-pixel corner detection algorithm for chessboard image,” International Conference on Measurement, Information and Control, vol. 2, pp. 1408-1411, August 2013.