Feature-Level Multi-focus Image Fusion using Neural Network and Image Enhancement
Image Processing applications have grown vastly in real world. Commonly due to limited depth of optical field lenses, it becomes inconceivable to obtain an image where all the objects are in focus. Image fusion deals with creating an image where all the objects are in focus. After image fusion, it plays an important role to perform other tasks of image processing such as image enhancement, image segmentation, and edge detection. This paper describes an application of Neural Network (NN), a novel feature-level multifocus image fusion technique has been implemented, which fuses multi-focus image using classification. The image is divided into blocks. The block feature vectors are fed to feed forward NN. The trained NN is then used to fuse any pair of multi-focus images. The implemented technique used in this paper is more efficient. The comparisons of the different existing approaches along with the implementing method by calculating different parameters like PSNR,RMSE.
