الفهرس 
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Abstract
The advancement of medical imaging has led to the acquisition of image data from
multiple modalities, necessitating the development of robust algorithms for accurate
and reliable fusion of such diverse image sets. Medical image fusion plays a crucial role
in enhancing the clinical applicability of medical images by combining information from
different modalities into a single fused image that provides comprehensive and
instructive insights. In recent years, significant efforts have been devoted to expanding
the repertoire of image fusion algorithms, particularly in the absence of standardized
benchmarks and comprehensive code libraries that can support state-of-the-art
techniques.
This thesis presents different proposed Deep Learning (DL) image fusion algorithms
in medical imaging applications, ultimately contributing to improved healthcare
diagnostics. Our findings highlight the superior performance of specific DL techniques,
for fusing different medical image modalities and achieving excellent restoration quality.
The First Proposal presents a comprehensive performance analysis of different
medical image fusion techniques applied to a wide range of medical images.
The Second Proposal describes a medical image fusion technique based on Relative
Total Variation Decomposition (RTVD) that can concurrently maintain the texture and
contrast information of the input images.