The DeToxSTM aims to develop imaging processing tools able to denoise scanning tunneling microscopy
(STM) images. The STM has been considered the most important technique to image,
control and monitorize molecular systems with atomic resolution under a wide range of experimental
conditions that enable molecule visualization. Usually, it is a hard task to find the best
conditions to obtain high resolution images with low noise. An adequate STM image interpretation
can be affected by noise signals and therefore it is necessary to correct and eliminate them. The
technique needs a long-term temporal analysis (i.e. many hours) and the drift can be severe enough
to displace the scan window beyond the original regions of interest.
This project intends to develop new algorithms to denoise the images acquired with STM. The
innovation goal is to formulate the sparse denoise problem as a morphological component analysis
problem, where the components are related to the periodicity of use of the elements present in the
learned dictionary (periodic v.s. aperiodic). This can be solved recurring to recently proposed work
on efficient computation of convolutional sparse representations.

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