%0 Journal Article %1 Sunil_2015 %A Borse, Prof. Mrs.Megha Sunil %A Patil, Prof. Dr. Mrs. Shubhangi B. %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K (EN) (FIS) (MI) Deviation(SD) Entropy Fuzzy Inference Information Membership Mutual Standard System functions(mf) %N 2 %P 500--503 %R 10.17762/ijritcc2321-8169.150215 %T Comparison of Different Image Fusion Techniques for 2D MRI Images %U http://dx.doi.org/10.17762/ijritcc2321-8169.150215 %V 3 %X Image fusion is the process of combining relevant information from two or more images into a single image. The resulting image contains more information than the input images. Thus data fusion combines partial and varied information which is present in multiple images and forms a single image having the collective features of all the input images. It has two main aims which are removal of partial redundant data, as all sources provide information about the same phenomenon ;and Other is the complementarities between data as each source provides a different view about the same phenomenon. Two techniques are implemented for image fusion which are Wavelet Transform and Fuzzy Logic. The results of these techniques are compared based on Entropy, Standard Deviation and Mutual Information

@article{Sunil_2015, abstract = {Image fusion is the process of combining relevant information from two or more images into a single image. The resulting image contains more information than the input images. Thus data fusion combines partial and varied information which is present in multiple images and forms a single image having the collective features of all the input images. It has two main aims which are removal of partial redundant data, as all sources provide information about the same phenomenon ;and Other is the complementarities between data as each source provides a different view about the same phenomenon. Two techniques are implemented for image fusion which are Wavelet Transform and Fuzzy Logic. The results of these techniques are compared based on Entropy, Standard Deviation and Mutual Information}, added-at = {2015-08-04T06:48:25.000+0200}, author = {Borse, Prof. Mrs.Megha Sunil and Patil, Prof. Dr. Mrs. Shubhangi B.}, biburl = {https://www.bibsonomy.org/bibtex/278f47e26e2f80ed69829faa710716337/ijritcc}, doi = {10.17762/ijritcc2321-8169.150215}, interhash = {f58f5994943606a03019275db69f2bf9}, intrahash = {78f47e26e2f80ed69829faa710716337}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {(EN) (FIS) (MI) Deviation(SD) Entropy Fuzzy Inference Information Membership Mutual Standard System functions(mf)}, month = {february}, number = 2, pages = {500--503}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-04T06:48:25.000+0200}, title = {Comparison of Different Image Fusion Techniques for 2D {MRI} Images}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.150215}, volume = 3, year = 2015 }