| Summary: | 碩士 === 國立交通大學 === 資訊科學學系 === 83 === Image interpolation for reconstructing images from low resolut-
ion to high resolution is an important processing step for many
applications. The image interpolation process can be viewed as
a transformation function, called interpolation function, from
input subsampled image to interpolated image. During the past
years, a lot of approaches using some pre-specified and non-
adaptive function models are proposed. In this thesis, the
method based on neural network with learning property is
different from the conventional approaches. Because that the
problem input is the subsampled image only and the target
output is unknown in the real-world application, it is
difficult to decide the optimal sample set for neural network
training. However, the projection model of image acquisition is
proposed and applied to the generation of training samples with
a window scanning in the input image. Thus, the image
interpolation process can be viewed and models as an inversion
of image acquisition. Based on this idea, our experimental
results demonstrate that our proposed methods are proven to be
useful and successful in solving this problem .
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