  Wavelets are waveform functions that describe transient and unstable
variations, such as noises. In this work, we study the advantages of discrete
and continuous wavelet transforms (DWT and CWT) of microlensing data to denoise
them and extract their planetary signals and intrinsic pulsations hidden by
noises. We first generate synthetic microlensing data and apply wavelet
denoising to them. For these simulated microlensing data with ideally Gaussian
nosies based on the OGLE photometric accuracy, denoising with DWT reduces
standard deviations of data from real models by $0.044$-$0.048$ mag. The
efficiency to regenerate real models and planetary signals with denoised data
strongly depends on the observing cadence and decreases from $37\%$ to $0.01\%$
by worsening cadence from $15$ min to $6$ hrs. We then apply denoising on $100$
microlensing events discovered by the OGLE group. On average, wavelet denoising
for these data improves standard deviations and $\chi^{2}_{\rm n}$ of data with
respect to the best-fitted models by $0.023$ mag, and $1.16$, respectively. The
best-performing wavelets (based on either the highest signal-to-noise ratio's
peak ($\rm{SNR}_{\rm{max}}$), or the highest Pearson's correlation, or the
lowest Root Mean Squared Error (RMSE) for denoised data) are from 'Symlet', and
'Biorthogonal' wavelets families in simulated, and OGLE data, respectively. In
some denoised data, intrinsic stellar pulsations or small planetary-like
deviations appear which were covered with noises in raw data. However, through
DWT denoising rather flattened and wide planetary signals could be
reconstructed than sharp signals. CWT and 3D frequency-power-time maps could
advise about the existence of sharp signals.
