Bioimage postprocessing based on discrete wavelet transform and Lucy-Richardson deconvolution methods

Accompanied with the increasing requirements of probing micro/nanoscopic structures of biological samples, a variety of image processing algorithms have been developed for visualization or to facilitate data analysis. However, it remains challenging to enhance both the signal-to-noise ratio and image resolution using a single algorithm. In this investigation, we propose an approach utilizing discrete wavelet transform (DWT) in combination with Lucy-Richardson (LR) deconvolution (DWDC). Our results demonstrate that the signal-to-noise ratio and resolution of live cell9 s microtubule network are considerably improved, allowing recognition of features as small as 120 nm. Notably, the approach is independent of imaging system and shows robustness in processing fibrous structures, e.g. the cytoskeleton networks.