  Rotating radio transients (RRATs) are sporadically emitting pulsars
detectable only through searches for single pulses. While over 100 RRATs have
been detected, only a small fraction (roughly 20\%) have phase-connected timing
solutions, which are critical for determining how they relate to other neutron
star populations. Detecting more pulses in order to achieve solutions is a key
to understanding their physical nature. Astronomical signals collected by radio
telescopes contain noise from many sources, making the detection of weak pulses
difficult. Applying a denoising method to raw time series prior to performing a
single-pulse search typically leads to a more accurate estimation of their
times of arrival (TOAs). Taking into account some features of RRAT pulses and
noise, we present a denoising method based on wavelet data analysis, an
image-processing technique. Assuming that the spin period of an RRAT is known,
we estimate the frequency spectrum components contributing to the composition
of RRAT pulses. This allows us to suppress the noise, which contributes to
other frequencies. We apply the wavelet denoising method including selective
wavelet reconstruction and wavelet shrinkage to the de-dispersed time series of
eight RRATs with existing timing solutions. The signal-to-noise ratio (S/N) of
most pulses are improved after wavelet denoising. Compared to the conventional
approach, we measure 12% to 69% more TOAs for the eight RRATs. The new timing
solutions for the eight RRATs show 16% to 90% smaller estimation error of most
parameters. Thus, we conclude that wavelet analysis is an effective tool for
denoising RRATs signal.
