In summary , we have developed DiLFM , an algorithmenhanced LFM technique that can substantially reduce reconstruction artifacts and maintain high contrast without any hardware modification even in extremely noisy conditions . To optimize the performance of the proposed DiLFM , we thoroughly discuss the appearance and mechanism of three different kinds of LFM reconstruction artifacts and intake them all into a dictionary patching model to correct them . Furthermore , the proposed dictionary patching increases the reconstruction resolution and contrast by supplying high - resolution and high - contrast information from the training stage . We validate our DiLFM through In the functional imaging experiment , we show DiLFM can discover two times more neurons with improved ΔF / F and reduced artifacts disturbance with low light dosage . We hope the scheme can help LFM become a promising and reliable tool for high - speed imaging biological tissues in 3D. The proposed DiLFM achieves superior performance compared to traditional LFM but with full advantages of LFM in other aspects . For example , the volume acquisition rate of DiLFM is independent of the size of the sample and only limited by camera frame rate , compared to other 3D imaging technology . Introducing the dictionary only affects the downstream data processing speed without any sacrifice of capturing rate . It is straightforward to extend DiLFM to a larger FOV or a compact head - mounted LFM 27 . Furthermore , by introducing photon - scattering models into dictionary priors 34 , it is possible to exceed the depthpenetration limitation of DiLFM in in vivo mouse - brain imaging 23 . Borrowing the thoughts from DiLFM of using the versatile dictionary to adopt different imaging environments , other deconvolution energized volumetric imaging methods 22,35 can also use such a prior for better performance in various applications .