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Alternatively, Fig. 4b shows the AIM image in 4d reconstructed on a linear scale.
Images were analyzed using the Carl Zeiss AIM Image Examiner Version 4.2.
The problem of saturation is completely remedied in Fig. 6 (bottom) where we observe that the AIM image not only avoids saturation in the strong sample regions but does so without sacrificing SNR in the weak sample regions.
To properly compare both images on the same scale, either a logarithm can be applied numerically to the conventional image, or an anti-logarithm can be applied numerically to the AIM image.
The log AIM image of course exhibits increased visibility in weakly fluorescent areas simply due to the logarithmic scale, however the benefits of AIM go much beyond this simple change of scale.
Representing reconstructed AIM data in a single channel allows AIM to be more readily applied as a drop-in enhancement to a wider selection of instruments, since no software reconstruction is required to visualize the AIM image in real time, and not all microscope systems have two available input channels.
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A log-scale output is thus well-suited to AIM images, since this matches the characteristic of good low signal precision and high overall range.
The aim of image quality optimisation is to provide an image that is suitable for the clinical task with the lowest radiation dose given to the patient.
The aim of image compression [1] is to reduce the data size of image and then make the image stored or transmitted in an efficient form.
The aim of image registration is to apply spatial transformations to the input images to bring them into alignment with the base image.
The aim of image fusion is to integrate complementary as well as redundant information from multiple images to create a fused image output.
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