Optimized lifting schemes based on ENO stencils for image approximation
Mounir Kaaniche, Basarab Mateï, Sylvain Meignen · 2015
In this paper, we propose to improve the classical lifting-based wavelet transforms by defining three classes of pixels which will be predicted differently. More specifically, the proposed idea is inspired by the Essentially Non-Oscillatory (ENO) transform and consists in shifting the stencil used for prediction in order to reduce the error near image singularities. Moreover, the different filters associated with these classes will be optimized in order to design a multiresolution representation well adapted to image characteristics. Our simulations show that the resulting multiscale representation leads to much lower amplitudes of the detail coefficients and improves the linear approximation properties.