The lifting scheme reduces the computational complexity of the discrete wavelet transform (DWT) by factoring the wavelet filters into cascades of simple lifting steps that process the input samples in pairs. We developed four compact and efficient hardware architectures for implementing lifting-based DWTs, namely, 1-D and 2-D versions of what we call recursive and dual-scan architectures. The 1-D recursive architecture exploits interdependencies among the wavelet coefficients by interleaving, on alternate clock cycles using the same datapath hardware, the calculation of higherorder coefficients along with that of the first-stage coefficients. The resulting hardware utilization exceeds 90% in the typical case of a 5-stage 1-D DWT operating on 1024 samples. The 1-D dual-scan architecture increases the datapath hardware utilization to 100% by processing two independent data streams together using shared functional blocks. The recursive and dual-scan architectures can be readily extended to the 2-D case. The 2-D recursive architecture is roughly 25% faster than conventional implementations, and it requires a buffer that stores only a few rows of the data array instead of a fixed fraction (typically 25% or more) of the entire array. The 2-D dualscan architecture processes the column and row transforms simultaneously, and eliminates the memory buffer for the row transform coefficients.