The fuzzy restricted Boltzmann machine (FRBM) is demonstrated to have better generative and discriminative capabilities than traditional RBM. We now further investigate and compare the generative ability of DBN with FRBM on image reconstruction. The DBN is pre-trained by stacking RBMs layer by layer and then fine-tuned by the wake-sleep algorithm. Then the FRBM, RBM and DBN are compared in detail under different conditions on the MNIST and Extended Yale B data sets. The experiment results again indicate that the FRBM outperforms RBM: it can achieve smaller average reconstruction errors (AREs) given the same number of hidden units and learning time. When compared to DBNs, the FRBM can achieve smaller AREs in less learning time than the two-layer DBN with equal hidden size. Moreover, when we increase the training epochs, the FRBM shows a better ARE and a slight increase of (or still less) learning time than corresponding two-layer and three layer DBNs with double number of hidden units. Hence we make a preliminary conclusion that the FRBM with m hidden units possesses the close generative capability to DBN with 2m hidden units in reconstructing images.