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University of Tokyo researchers developed a frequency-based “inpainting” method that considers both frequency and spatial information to generate missing portions of images. The technique is a synthesis of two distinct restorative qualities, giving it significant advantages over currently used methods that use only spatial domain information during the learning process. That can cause the loss of interior reconstruction details, resulting in the estimation of only a low-frequency part of the original patch.
The Tokyo researchers showed that frequency-based image inpainting sufficed to convert inpainting to deconvolution in the frequency domain. The effect of the process enabled the accurate prediction of the local…READ MORE