Polyphase interpretation of empirical image interpolation

10 years 4 months ago
Polyphase interpretation of empirical image interpolation
We observe several characteristics of empirical image interpolating algorithms and contribute four novel concepts and claims. First, we interpret well-known classification-based filtering algorithms in terms of their polyphase components. We examine the underlying principles behind the various fixed-scale linear interpolating kernels. Second, we conceptually extend the properties of the multiple filters to two dimensions to analyze frequency domain characteristics common to all empirically-designed interpolating filters. Third, we propose a general linear filter for image interpolation, which uses a universal magnitude response and zero-phase. Finally, the proposed filter is further generalized to support arbitrary scaling factors. We claim that at any scaling factor, the proposed algorithm yields lowcomplexity at a minimal loss of high image-quality with the ability to interpolate diverse image content.
Karl S. Ni, Truong Q. Nguyen
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Authors Karl S. Ni, Truong Q. Nguyen
Comments (0)