Image fusion of noisy images based on simultaneous empirical wavelet transform

Abstract

Fusion image is the method of extracting the relevant information from two or more identical input images into one scene and creating a new image. This method allows the new image to provide comprehensive information about the wand, leading to a visual understanding of the human being. Fusion image application in image processing is an important issue. Applications in many fields such as photography, microscopy, astronomy, medical imaging, satellite imagery, machine vision, biology are monitored. In this study first, an image fusion method, suggested recently based on transform empirical wavelet, was implemented in which coefficients were obtained by processing the input images. Then they were combined by applying the rules. The aim of this study is to investigate the noise effect and to remove the noise in the aforementioned suggested method. First, the noise was added to the images, and the images were decomposed into layers or coefficients. Second, by thresholding the coefficients, the noise was removed. Then the coefficients were combined based on the rules to obtain the final coefficients. In the end, the final coefficients were used to obtain the fused image. The results show that the noise removal of images during image fusion is much better and more effective than denoising before image fusion, and the demonstration of the method is proved by obtaining better results in comparison to some existing methods.