Image Registration based on Neural Network and Fourier Transform

Abstract

An image registration technique based on feed forward neural network and Fourier Transform is developed and presented. In the proposed scheme, the spectrums of the acquired images are computed, the Fourier coefficients within a selected central window of each spectrum are extracted and fed as inputs to the neural network. The feed forward neural network is implemented to estimate the transformation, defined in terms of the translation, rotation and magnification parameters, to align the corresponding images. This approach does not estimate the various registration parameters separately.They are estimated simultaneously leading to a better-optimized set of registration parameters. The approach is successful and yields better results than another Fourier based registration technique. The approach is validated on 2D images. However, it can be easily extended to 3-D application.

FMRI 3D registration based on Fourier space subsets using neural networks

“…Abche indicates that the simultaneous determination of the registration parameters may lead to a better-optimized set of estimates [12]. However, our preliminary results on this issue do not support that strategy (results not presented here).…”

“…A NN-based method which relies on Fourier coefficients has also been proposed by Abche and co-workers for 2D registration of MRI images [12].…”

An efficient registration and fusion algorithm for large misalignment remote sensing images

“…But the problem is that it needs to estimate the probability density, therefore, it is time-consuming. The second class of registration methods is Fourier transform based registration [4,5] , which is suitable for linear variation of the grey levels such as images with low frequency noise or with the grey level change produced by rotation, translation and scale variation. The third class is feature-based registration.…”

Efficient and accurate image alignment using TSK-type neuro-fuzzy network with data-mining-based evolutionary learning algorithm

“…. .+wnjxn (4) The structure of a TNFN is shown in Figure 3, where n represents the dimension of the input. It is a five-layer network structure.…”

“…Thus, it raises a challenge to provide an efficient affine transformation. To this end, neural network-based methods have widespread to address this challenge because such methods often feed global features of inspected images into a trained neural network to estimate affine transformation parameters [1][2][3][4]. In other words, neural networks are helpful for designing image alignment systems.…”