Image Registration Using Log Polar Transform and Phase Correlation to Recover Higher Scale

Image registration is an important and fundamental task in image processing used to match two different images. Given two or more different images to he registered, image registration estimates the parameters of the geometrical transformation model that maps the sensed images back to its reference image. In all types of image registration, robustness of the algorithm is the main and required goal. Image registration is the process of spatially aligning two or more images of a scene taken at different times or with different sensors or from different viewpoints. This basic capability is needed in various image analysis applications which include remote sensing, medical image analysis, object recognition, etc. in this paper, we have proposed an algorithm that is based on Log-Polar Transform (LPT) and Phase Correlation to register images which are transformed by rotation, translation and higher value of scale. In the proposed algorithm, first we roughly estimate the scale between the images using loeHgabor function and then downscale the sense image by scale factor 2. The proposed algorithm can recover scale value up to 4.10. The robustness of this algorithm is verified on different images with similarity transformation, partial data and in the presence of noise.