Compressed fluorescence lifetime imaging via combined TV-based and deep priors

Abstract

Compressed fluorescence lifetime imaging (Compressed-FLIM) is a novel Snapshot compressive imaging (SCI) method for single-shot widefield FLIM. This approach has the advantages of high temporal resolution and deep frame sequences, allowing for the analysis of FLIM signals that follow complex decay models. However, the precision of Compressed-FLIM is limited by reconstruction algorithms. To improve the reconstruction accuracy of Compressed-FLIM in dealing with large-scale FLIM problem, we developed a more effective combined prior model 3DTGp V_net, based on the Plug and Play (PnP) framework. Extensive numerical simulations indicate the proposed method eliminates reconstruction artifacts caused by the Deep denoiser networks. Moreover, it improves the reconstructed accuracy by around 4dB (peak signal-to-noise ratio; PSNR) over the state-of-the-art TV+FFDNet in test data sets. We conducted the single-shot FLIM experiment with different Rhodamine reagents and the results show that in practice, the proposed algorithm has promising reconstruction performance and more negligible lifetime bias.

Single-sample image-fusion upsampling of fluorescence lifetime images

“…Prior constraints on the target high-resolution lifetime image are enforced through the second and the third data fidelity term, weighed by the factor γ and β, respectively, which are empirically optimized to yield best results. The fourth term is the l 1 norm of the two-dimensional total variation (TV) evaluated on the high-resolution lifetime image and weighed by α ( 59 ). We consider the anisotropic form of the TV ( 60 ), and so the operator D represents the finite differences approximation of the horizontal and vertical image gradients.…”