Recently multiple high performance algorithms have been developed to infer high-resolution images from low-resolution image input using deep learning algorithms. The related problem of super-resolution from blurred or corrupted low-resolution images has however received much less attention. In this work, we propose a new deep learning approach that simultaneously addresses deblurring and super-resolution from blurred low resolution images. We evaluate the state-of-the-art super-resolution convolutional neural network (SR-CNN) architecture proposed in [1] for the blurred reconstruction scenario and propose a revised deeper architecture that proves its superiority experimentally both when the levels of blur are known and unknown a priori.