A HYBRID APPROACH FOR MULTIVIEW IMAGE FUSION USING DWT, PCA AND NEURAL NETWORK

Abstract: Image Fusion is the process in which core information from a set of component images is merged to form a single image, which is more informative and complete than the component input images in quality and appearance. This research presents a fast and effective image fusion method for creating high quality fused images by merging component images. In the proposed method, the input image is broken down to a two-scale image representation with a base layer having large scale variations in intensity, and a detail layer containing small scale details. Here fusion of the base and detail layers is implemented by means of a Local Edge preserving filtering based technique. The proposed method is an efficient image fusion technique in which the noise component is very low and quality of the resultant image is high so that it can be used for applications like medical image processing, requiring very accurate edge preserved images. Performance is tested by calculating PSNR, entropy, MSE and SSIM of images. The benefit of the proposed method is that it removes noise without altering the underlying structures of the image. Experimental results showed that the when PSNR value is calculated, the noise ratio is found to be very low for the resultant image portion. All the simulations have been carried out in MATLAB simulator tool.

Keywords: Image fusion, DWT, PCA, BPNN, PSNR, Entropy, MSE and SSIM