  Context. The presence of clouds during observations with Imaging Atmospheric
Cherenkov Telescopes can strongly affect the performance of the instrument due
to additional absorption of light and scattering of light beyond the field of
view of the instrument. If not corrected for, the presence of clouds leads to
increased systematic errors in the results. Aims. One approach to correct for
the effects of clouds is to include clouds in Monte Carlo simulations to
produce models for primary particle classification, energy and direction
estimation. However, this method is challenging due to the dynamic nature of
cloudy conditions and requires extensive computational resources. The second
approach focuses on correcting the data itself for cloud effects, which allows
the use of standard simulations. However, existing corrections often prioritise
limiting systematic errors without optimising overall performance. By
correcting the data already at the image level, it is possible to improve event
reconstruction without the need for specialised simulations. Methods. We
introduce a novel analysis method, based on a geometrical model that can
correct the data already at the image level given a vertical transmission
profile of a cloud. Using Monte Carlo simulations of an array of four
Large-Sized Telescopes of the Cherenkov Telescope Array, we investigate the
effect of the correction on the image parameters and the performance of the
system. We compare the data correction at the camera level with the use of
dedicated simulations for clouds with different transmissions and heights.
Results. The proposed method efficiently corrects the extinction of light in
clouds, eliminating the need for dedicated simulations. Evaluation using Monte
Carlo simulations demonstrates improved gamma-ray event reconstruction and
overall system performance.
