A neural network approach for image restoration is presented. The proposed method is based on a neural network with hierarchical cluster architecture (NNHCA), one of the recently emerged neural networks with sophisticated architectures. The method is motivated by the universally accepted concept in digital image processing that natural image formation is a local process. Therefore, the inverse problem of image restoration can be expressed by a globally coordinated local parallel processing (GCLPP) model. The GCLPP model can be readily realized by NNHCA. By utilizing the symmetric positive-definite quadratic structure of the formulation, a model-based local neuron evaluation algorithm is proposed. The algorithm significantly increases the convergence speed of restoration compared with previously proposed neural computing methods. A coordination scheme is also introduced to systematically resolve conflicting boundary conditions in the problem formulation. Visual examples are given to demonstrate that the proposed method not only produces good restoration results, but also provides a genuine parallel processing structure that ensures computationally feasible space domain image restoration.