Improved superresolution microscopy imaging by sparse deconvolution with an interframe penalty

Penalized regression with a combination of sparseness and an interframe penalty is explored for image deconvolution in wide-field single-molecule fluorescence microscopy. The aim is to reconstruct superresolution images, which can be achieved by averaging the positions and intensities of individual fluorophores obtained from the analysis of successive frames. Sparsity of the fluorophore distribution in the spatial domain is obtained with an L-0-norm penalty on estimated fluorophore intensities, effectively constraining the number of fluorophores per frame. Simultaneously, continuity of the fluorophore localizations in the time mode is obtained by penalizing the total numbers of pixel status changes between successive frames. We implemented the interframe penalty in a sparse deconvolution algorithm (sparse image deconvolution and reconstruction) for improved imaging of densely labeled biological samples. For simulated and real biological data, we show that more accurate estimates of the final superresolution images of cellular structures can be obtained. By combining multiple penalties in a regression framework for image deconvolution of wide-field single-molecule fluorescence microscopy, more accurate estimates of final superresolution images of cellular structures are obtained. Sparsity of the fluorophore distribution in the spatial domain is ensured by using an L0-norm penalty. Continuity of the localizations in the time mode is obtained by penalizing the total number of pixel status changes between successive frames.