One disadvantage of all fluorescence imaging modalities is a poor axial resolution. To overcome this issue, we propose a novel approach to reconstruct fluorescence volumes with isotropic high resolution, from images of particle replicates observed at multiple orientations. We design a joint deconvolution-and-multiview reconstruction approach dedicated to three-dimensional fluorescence imaging. We address the computational challenge associated to big data by designing a fast augmented-Lagrangian optimizer. The computational cost of the iterative part of the algorithm does not depend on the number of input particles. We experimentally demonstrate the resolution improvement yielded by our framework and its ability to handle practical constraints like large PSF sizes and large number of particles. The validation is performed on realistic simulated data, which establishes a proof of concept for our framework and defines it as the basis for future extensions.