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The proposed method utilizes the SSIM index as a criterion for reconstructing missing areas in the target image.
Interpolation and extrapolation methods use the texture of neighbor pixels around the holes to fill missing areas in the warped images [12].
Then, for some test images, since target patches contain missing areas in the whole parts, those methods cannot perform inpainting on those missing areas.
Restoration of missing areas in digital images has been intensively studied since it can be applied to a number of fundamental applications such as restoration of corrupted old films, removal of unnecessary objects, and error concealment.
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In this subsection, we present the reconstruction algorithm of the missing area in the target patch based on the SSIM index.
The key point of this method is an algorithm that iteratively estimates the missing area in the transformed domain according to the parts where data are completely recorded.
Multiplying this average by 50 cm provides an estimate of 1260 cm3 for the volume of the missing area in UALVP 47273.
To overcome the limitations of these diffusion methods, exemplar methods combine both structural and textural properties when filling in missing areas [17].
You can review the case yourself and assess any gaps or missing areas by putting yourself in the role of Tester.
It can also be a stimulus for spending those saved costs on more qualified specialists in the missing areas instead.
In this subsection, the inpainting algorithm of the missing area Ω in the target patch f based on the SSIM index is presented.
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