A STUDY ON EIGEN FACES FOR REMOVING IMAGE BLURREDNESS THROUGH IMAGE FUSION

Advances in technology have brought about extensive research in the field of image fusion. Image fusion is one of the most researched challenges of Face Recognition. Face Recognition (FR) is the process by which the brain and mind understand, interpret and identify or verify human faces Face recognition is nothing but a biometric application by which we can automatically identify and recognize a person from the visual image of that person stored in the database. Image fusion is the perfect combination of relevant information from two or more images into a single fused image. As a result the final output image will carry more information as compare to the input images. Thus the main aim of an image fusion algorithm is to take redundant and complementary information from the source images and to generate an output image with better visual quality. In this paper we have proposed a novel approach of pixel level image fusion using PCA that will remove the image blurredness in two images and reconstruct a new de-blurred fused image. The proposed approach is based on the calculation of Eigen faces with Principal Component Analysis (PCA). Principal Component Analysis (PCA) has been most widely used method for dimensionality reduction and feature extraction.