Wireless sensor networks provide a natural application area for distributed data processing algorithms. Power consumption for communication between sensor network nodes typically dominates over that for local data processing, so it is often more efficient to process data in the network than it is to send data to a remote, central collection point for analysis. Distributed wavelet analysis represents one such technique, whereby local collaboration among nodes de-correlates measurements, yielding a sparser data set with fewer significant values. This sparsity can then be leveraged to suppress errors in nodes' measurements, which are typically gathered by inexpensive sensors subject to measurement noise. In this paper, we briefly review the details of a distributed wavelet processing protocol for sensor networks based on the theory of lifting, and we develop a suite of wavelet de-noising protocols for distributed de-noising of measurements. We illustrate the effectiveness of the system with a series of numeric examples.