In 3D fluorescence microscopy, the axial resolution (axis Z) is often lower than the lateral resolution (optical plane XY). This non-isotropic resolution penalizes the global resolution of the biological structures. The conventional way to expand the size is to interpolate in the Z axis which create blurry 3D images without recovering any structure. In this project, we propose to recover the axial resolution based on the deep learning method. We propose a 2D strategy by patches to build a reasonable system. The learning will be performed in the 2D XY plane (higher resolution) than the reconstruction in performed in the 2D XZ or/and in the YZ planes. The learning with be done using a deep convolution neuronal network (CNN) in particular using a multiresolution approach with the U-net architecture. This recent architecture has demonstrated very good results in many fields.