Voltage imaging is an emerging microscopy technique that can make neuroscientific research very prominent. The images obtained with this imaging method exhibit a substantial amount of noise.
Currently, the new methods are developed and tested to computationally denoise voltage imaging videos with high efficiency and preservation of the video structure.
This research attempted to investigate how well traditional denoising algorithms, such as different types of blur or diffusion, perform in denoising such videos. Specifically, five well-established algorithms commonly used in biomedical imaging were evaluated: Gaussian filter, bilateral filter, anisotropic diffusion, wavelet filter, and total variation minimization.
The methods were applied to both real brain recordings (HPC2 dataset) and synthetic videos (Broad DSP CellMincer).
Performance was assessed by measuring the structural similarity index (SSIM) and signal-to-noise ratio (SNR).
Results suggest a trade-off between noise removal and structural preservation, with total variation minimization and anisotropic filtering performing particularly well in terms of noise suppression. These classical methods remain relevant for data exploration and visualization.
For the methods to be used in a medical context, a more in-depth research should be carried out on medical data. The deep learning methods remain relevant for the high-precision applications. Code developed for the research is available online at https://github.com/Rpplctns/denoising-voltage-imaging-videos.git.