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Image fusion is a process that allows for the synthesis of information from multiple source images into a single image. There are many applications for image fusion including night vision, medical imaging, and remote sensing. Over the many applications, numerous image fusion algorithms have been explored from averaging pixel intensities to fusion through multi-resolution decomposition transforms such...
Image fusion has many applications in which a reference image is not always available including image registration, medical imaging, and fusion between visible and infrared imagery. For these no-reference applications, it is important that there are objective and efficient methods for validating fusion performance, as subjective image fusion evaluation is time consuming and non-scalable. There have...
As digital media and internet use grow, imagery and video are prevalent in many areas of life. Many sensing methods such as Full Motion Video (FMV), Hyperspectral Imagery (HSI), and medical imaging have been developed to accumulate data for diagnostics. Analyzing imagery data to detect and identify specific objects is an essential phase of comprehending visual imagery. Content-based image retrieval...
Extracting an optimal amount of information from a blurred image without a reference image for comparison is an pressing issue in image quality enhancement. Most studies have approached deblurring by using iterative algorithms in an attempt to deconvolve the blurred image into the ideal image. Deconvolution is difficult due to the need to estimate a point spread function for the blur after each iteration,...
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