Image Resampling and Interpolation based on Fuzzy Neural Network with Mapping Windows

• Main Authors: Shu-Fang Hsu, 許淑芳
• Other Authors: Pei-Yung Hsiao Ja-Chen Lin
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/33790745071683136335

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 .