International Journal of Engineering and Applied Sciences (IJEAS), 2025
The Image fusion is widely acknowledged as a useful tool for enhancing overall system performance... more The Image fusion is widely acknowledged as a useful tool for enhancing overall system performance in a variety of application areas such as battlefield surveillance, camouflaged ordnance detection, non-destructive testing defect detection, remote sensing, traffic control, machine learning and health care applications to name few, its own. There are, however, drawbacks to the information gathered from single-modality medical imaging. Medical diagnosis cannot be aided by extensive lesion information from single-modality imaging, which inevitably results in annoyance and poor clinical diagnosis performance. Medical image fusion is a method for resolving this issue; it does so by merging the benefits and supplementary data of several models of source images, eliminating redundant data, and providing a more thorough, accurate lesion description to support specialists in diagnosis and decision-making. Medical image fusion, which merges multi-modal images using image processing, may be useful here. A multiresolution image fusion method uses the Spatial Frequency DCTWT (SFDCT-DWT) technology. In the SF-DCT-DWT technique, the lowresolution MRI image is resampled to the high-resolution CT image, and fusion is performed by injecting the spectral and spatial information from CT and MRI images onto each other using their DCT-DWT coefficients and spatial frequency analysis. These images are from MR and CT imaging. According to experimental data, the recommended strategy surpasses other subjective and objective measures including Entropy (EN), Mutual Information (MI), and Structural Similarity Index Measure (SSIM).
International Journal of Engineering and Applied Sciences, 2025
In many different application domains, image
fusion is widely acknowledged as a helpful technique... more In many different application domains, image fusion is widely acknowledged as a helpful technique for enhancing overall system performance. These application fields include, but are not limited to, remote sensing, traffic control, machine learning, health care applications, combat surveillance, detection of disguised munitions, and identification of faults in non-destructive testing. The amount of scholarly literature on the topic of medical image fusion has significantly increased during the last several years. Image fusion has grown in importance as a component of the commonly used image processing applications because to the large variety of capture devices that are currently available. Image fusion is the process of matching relevant information from several sensors using various mathematical models to create a single composite image. Combining data from many sensors, multiple viewpoints, and various temporal dimensions into a single image is known as image fusion. This preserves the integrity of important characteristics while improving the quality of the image. Robot vision, aerial, satellite, and medical imaging, as well as robot and vehicle navigation, are just a few of the many applications that heavily depend on this stage. This study examines many cutting-edge picture fusion methods at various levels, along with the benefits and drawbacks of each. It also explores several transform-based and spatial approaches with quality measures and their applicability across various industries. Finally, this study's results have shown several potential future application avenues, such as picture fusion applications.
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Papers by Riya Gupta
fusion is widely acknowledged as a helpful technique for
enhancing overall system performance. These application
fields include, but are not limited to, remote sensing, traffic
control, machine learning, health care applications, combat
surveillance, detection of disguised munitions, and
identification of faults in non-destructive testing. The amount
of scholarly literature on the topic of medical image fusion has
significantly increased during the last several years. Image
fusion has grown in importance as a component of the
commonly used image processing applications because to the
large variety of capture devices that are currently available.
Image fusion is the process of matching relevant information
from several sensors using various mathematical models to
create a single composite image. Combining data from many
sensors, multiple viewpoints, and various temporal dimensions
into a single image is known as image fusion. This preserves
the integrity of important characteristics while improving the
quality of the image. Robot vision, aerial, satellite, and medical
imaging, as well as robot and vehicle navigation, are just a few
of the many applications that heavily depend on this stage.
This study examines many cutting-edge picture fusion methods
at various levels, along with the benefits and drawbacks of
each. It also explores several transform-based and spatial
approaches with quality measures and their applicability
across various industries. Finally, this study's results have
shown several potential future application avenues, such as
picture fusion applications.