Authors: S. Sivasangumani
Addresses: Department of Electronics and Communication Engineering, MAR College of Engineering and Technology, Affiliated to Anna University, Pudhukottai, Tamil Nadu, 621316, India
Abstract: Image fusion is used to reduce the redundancy and increases the needed information in the processed image from two or more input images that have different information generated by different sources. The output image has more information and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion methods basically accept only registered images to produce a high quality fused single image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in medical applications. In this paper, we proposed an image fusion algorithm based on decision approach and NSCT to improve the future resolution of the images. In this, images will be segmented into regions and decomposed into sub-images and then processed using Fuzzy Logic, the information fusion is performed using these images under the certain criteria such as non subsampled contourlet transform (NSCT) and certain fusion rules such as Fuzzy Logic, and finally these sub-images are reconstructed into the resultant image with plentiful information. The various metrices entropy, mutual information (MI) and Fusion Quality are calculated to compare the results. The proposed method is compared both subjectively as well as objectively with the other image fusion methods. The experimental results show that the proposed method is better than other fusion methods and increases the quality and PSNR of fused image.
Keywords: image fusion; discrete wavelet transform; DWT; NSCT; non subsampled contourlet transform; fuzzy logic; genetics algorithm; entropy; mutual information; MI.
International Journal of Image Mining, 2018 Vol.3 No.2, pp.117 - 138
Received: 10 Apr 2017
Accepted: 28 Jan 2018
Published online: 22 Nov 2018 *