Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in signal/image processing. Wavelet transforms have excellent energy compaction characteristics and can provide perfect reconstruction. The shifting (translation) and scaling (dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated recursively, and in addition to the wavelet decomposition stage, extra space is required to store the intermediate coefficients. Hence, the overall performance depends significantly on the precision of the intermediate DWT coefficients. This work presents new implementation techniques of DWT, that are efficient in terms of computation, storage, and with better signal-to-noise ratio in the reconstructed signal.Computer-based exercises for signal processing using MATLAB. Englewood Cliffs: Prentice Hall ... Signal recovery from wavelet transform maxima. IEEE Transactions on ... Adaptive wavelet thresholding for image denoising and compression.
|Title||:||Efficient Algorithms for Discrete Wavelet Transform|
|Author||:||S K Shukla, Arvind K. Tiwari|
|Publisher||:||Springer Science & Business Media - 2013-01-26|