Lensless cameras, consisting of only a sensor and a mask, are small and flexible enough to be used in many applications with stringent scale constraints. These mask-based imagers encode scenes in caustic patterns. Most existing reconstruction algorithms rely on multiple iterations based on physical model for deconvolution followed by deep learning for perception, among which the main limitation of reconstruction quality is the mismatch between the ideal and the real model. To solve the problem, we in this work learned a class of multi Wiener deconvolution networks (MWDNs), deconvoluting in multi-scale feature spaces with Wiener filters to reduce the information loss and improving the accuracy of the given model by correcting the inputs. A comparison between the proposed and the state-of-the-art algorithms shows that ours achieves much better images and performs well in real-world environments. In addition, our method takes greater advantage of the computational time due to the abandonment of iterations.