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Edge-based image interpolation using symmetric biorthogonal wavelet transform

University of British Columbia

Image interpolation is an important part of digital image processing. Many approaches are proposed for enlarging and reducing images. Recently, most papers in image interpolation are focused on edge-based interpolation since sharp edges and smooth contours can give better impression to the human vision than others. L i & Orchard and Kimmel proposed edge-based interpolation approaches that can produce better image quality compared with the traditional methods such as bilinear and bicubic interpolations. In this thesis, a new edge-based image interpolation approach that uses symmetric biorthogonal wavelet transforms is proposed. According to wavelet multiresolution analysis theory, an image can be decomposed into a series of approximation sub-images and detail sub-images with horizontal, vertical, and diagonal edge information. Based on this theory, many wavelet-based interpolation approaches have been proposed. However, most of them are computationally expensive or not efficient. In this thesis, we set up a list of ideal step edge models, and explore the relationships between the wavelet approximation sub-image and the three wavelet detail sub-images of these models. Based on these relationships, a fast and efficient algorithm that predicts the edge information of the interpolated image is proposed. The results of our experiments prove that the wavelet-based image interpolation with our new approach has good performance compared with other state-ofthe- art image interpolation approaches. In conclusion, the 9 / 7 -M inverse wavelet transform with our new approach is the best solution for image interpolation.

Text

2009-12-16

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http://hdl.handle.net/2429/16825

Electrical and Computer Engineering

University of British Columbia

UBCV

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