Compressed sensing is an effective solution for signal acquisition and signal reconstruction at a much lower rate than the Nyquist rate. Traditional methods, such as orthogonal matching pursuit and basis pursuit, for image compressed sensing reconstruction have unsatisfying reconstruction quality and long reconstruction time. Researchers now focus on neural network and deep learning methods for the better reconstruction of compressed-sensed signals at a very low sampling rate and a fast speed. However, current neural network approaches for image compressed sensing do not consider the similarities between images or within images, or the types of image blocks; thus, performing poorly in images with complex contents. In this paper, we develop a novel neural network framework that utilizes the similarities between image blocks through Gaussian-mixture models without recording the similarity information to achieve better reconstruction quality than the state-of-the-art neural network methods for block-level image compressed sensing.