Monday

Motion magnification is a method for artificial magnification of low-amplitude quasi-periodic transverse displacements in the image sequence (imaging data cubes or videos). It acts like a microscope for low amplitude transverse motions making them much better visible in time-distance plots and animations. Our implementation of the motion magnification was developed for detecting and analysing low-amplitude transverse oscillations of coronal loops recently discovered in SDO/AIA data. Typical amplitude of these oscillations is below the spatial resolution of the instrument. Therefore, detecting and analysing them is a challenging task, and requires advanced data processing techniques.
The developed code is written in Python 3, and uses the 2D Dual Tree Complex Wavelet Transform (DTCWT). The input images are decomposed into a set of complex wavelet coefficients, and then the temporal phase variation is magnified by several times. The magnified data is reconstructed from the modified coefficients by the inverse DTCWT transform. The DTCWT parameters were tuned to provide the resolution of different motions in neighbouring structures.
The tests performed on the artificial data cubes showed the effectiveness, reliability, and robustness of this technique. In particular, the magnification is linear in a broad range of oscillation periods (about one decade), and amplitudes (0.1 - 1 pixel). The application of this technique to SDO/AIA data allowed for the improved detection of low-amplitude decay-less oscillations.
Though, the code is designed to work with EUV imaging observations of the Sun, it can be applied to any time sequence of images, i.e. an imaging data cube or video.