De-noising based on wavelet transform in Rayleigh-Taylor instability analysis

In hydrodynamics instability experiment, the quantificational relation between amplitude fluctuation of sample and X-ray intensity is important to analysing Rayleigh-Taylor instability exactly. The signal-to-noise of the data is always low due to the complex image environment, thus improving the signal-to-noise of the image is inevitable. With the area backlighting, the X-ray intensity images were recorded by XSC in ShenguangⅡ facility. Daubechies wavelet filtering and Wiener filtering were used to de-noise the data separately. The results show that the Daubechies wavelet filtering is better than the Wiener filtering in de-noising and maintaining the detail of the signal.