Multi-modal image fusion technique for enhancing image quality with multi-scale decomposition algorithm

Abstract

ABSTRACT Multi-modality image fusion is an effective technique for fusing complementary information from multi-modality images into an integrated image. The additional information obtained from fused images not only enhances visibility to the human eye, but also complements the limitations of each image. The existing image fusion techniques lacks in preserving the quality of image. Therefore to preserve the structure information and perform the detailed information of source images, a novel image fusion scheme based on Multi-Scale Singular Value Decomposition (MSVD) is proposed. Initially, source images are decomposed into detailed and smooth layer by means of MSVD. Then low- and high-frequency components are extracted with the help of proposed filtering technique and subtraction rule. For efficiently selecting appropriate components, Optimisation algorithm is performed by maximising the mutual information of images. Finally, based on additive fusion rule, the fused images are reconstructed through inverse of MSVD approach. The efficiency of the proposed fusion rule is determined by the comparative analysis with existing image fusion method in terms of Mutual Information (MI), Standard Deviation, Entropy, Correlation etc. The experimental outcome shows the superiority of proposed method and it outperforms state-of-the-art methods by providing better quality image with good clarity. Furthermore, the fused image illustrates better visual effect and edge consistency.