List of Articles Image registration

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  1 - Simultaneous Methods of Image Registration and Super-Resolution Using Analytical Combinational Jacobian Matrix
  Hossein  Rezayi Seyed Alireza  Seyedin
  10.7508/jist.2015.03.008
  In this paper we propose two new simultaneous image registration (IR) and super-resolution (SR) methods using a novel approach to calculate the Jacobian matrix. SR is the process of fusing several low resolution (LR) images to reconstruct a high resolution (HR) image; h More

  In this paper we propose two new simultaneous image registration (IR) and super-resolution (SR) methods using a novel approach to calculate the Jacobian matrix. SR is the process of fusing several low resolution (LR) images to reconstruct a high resolution (HR) image; however as inverse problem it consists of three principal operations of warping, blurring and down-sampling should be applied to the desired HR image to produce the existing LR images. Unlike the previous methods, we neither calculate the Jacobian matrix numerically nor derive the Jacobian matrix by treating the three principal operations separately. We develop a new approach to derive the Jacobian matrix analytically from combinational form of the three principal operations. In this approach, a Gaussian kernel (as it is more realistic in a wide rang of applications) is considered for blurring, which can be adaptively resized for each LR image. The main intended method is established by applying the aforementioned ideas to the joint methods, a class of simultaneous iterative methods in which the incremental values for both registration parameters and HR image are obtained by solving one system of equations per iteration. Our second proposed method is formed by applying these ideas to the alternating minimization (AM) methods, a class of simultaneous iterative methods in which the incremental values of registration parameters are obtained after calculating the high resolution image at each iteration. The results show that our methods are superior to the recently proposed methods such as Tian's joint and Hardie's AM method. Additionally, the computational cost of our proposed methods has also been reduced. Manuscript profile
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  2 - Remote Sensing Image Registration based on a Geometrical Model Matching
  Zahra Hossein-Nejad Hamed Agahi Azar Mahmoodzadeh
  10.52547/jist.9.36.41
  Remote sensing image registration is the method of aligning two images from the same scene taken under different imaging circumstances containing different times, angles, or sensors. Scale-invariant feature transform (SIFT) is one of the most common matching methods pre More

  Remote sensing image registration is the method of aligning two images from the same scene taken under different imaging circumstances containing different times, angles, or sensors. Scale-invariant feature transform (SIFT) is one of the most common matching methods previously used in the remote sensing image registration. The defects of SIFT are the large number of mismatches and high execution time due to the high dimensions of classical SIFT descriptor. These drawbacks reduce the efficiency of the SIFT algorithm. To enhance the performance of the remote sensing image registration, this paper proposes an approach consisting of three different steps. At first, the keypoints of both reference and second images are extracted using SIFT algorithm. Then, to increase the speed of the algorithm and accuracy of the matching, the SIFT descriptor with the vector length of 64 is used for keypoints description. Finally, a new method has been proposed for the image matching. The proposed matching method is based on calculating the distances of keypoints and their transformed points. Simulation results of applying the proposed method to some standard databases demonstrated the superiority of this approach compared with some other existing methods, according to the root mean square error (RMSE), precision and running time criteria. Manuscript profile
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  3 - A Multi-Resolution Learning Based Method for Multimodal Medical Image Registration
  S. S. Alehojat Khasmakhi M. R.  Keyvanpour
  The main purpose in various methods of image registration is to find the transformation parameters for accurate mapping an image onto another image coordinates. In medical sciences creating a precise mapping between medical images data is very important in application More

  The main purpose in various methods of image registration is to find the transformation parameters for accurate mapping an image onto another image coordinates. In medical sciences creating a precise mapping between medical images data is very important in application such as diagnosis and treatment. Accordingly, several approaches have been proposed for image registration. The compression of results and performance between different image registration algorithms was the main motivation for this research to design and implement a new hybrid algorithm so that provide high accuracy in multimodal image registration. Automating the image registration process by using machine learning approach is the innovation of this method compared to previous ones. To this end, the proposed method which is named multi resolution learning is composed of multi resolution decomposition and a hierarchical neural network which it learn the transformation parameters by using global properties of the image and uses learned transformation parameter for image registration. The proposed method is implemented and tested on the medical images of Vanderbilt university database. Experiment result show acceptable accuracy for the proposed method compared with other methods. Manuscript profile