International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Modal Feature Disentanglement and Contribution Estimation for Multimodality Image Fusion (MFDCE-Fuse) |
|---|---|
| ABSTRACT | Multimodality image fusion (MMIF) tasks aim to fuse complementary information from different modalities, such as salient objects and texture details, to improve image quality and information comprehensiveness. Most current MMIF methods adopt a “black-box” decoder to generate fused images, which leads to insufficient interpretability and difficulty in training. To deal with these problems, MMIF is converted into a modality contribution estimation task through a novel self-supervised fusion network named MFDCE-Fuse. |
| AUTHOR | D. SRIDHAR, T.SIRISHA, K.HEMA, G.MOKSHAGNA, M.PRUDHVI NARAYANA U.G. Student, Department of ECE, SVIET Engineering College, Nandamuru, Pedana, Andhra Pradesh, India Associate Professor, Department of ECE, SVIET Engineering College, Nandamuru, Pedana, Andhra Pradesh, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1403129 |
| pdf/129_ Modal Feature Disentanglement and Contribution Estimation for Multimodality Image Fusion (MFDCE-Fuse).pdf | |
| KEYWORDS | |
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