Linear space-invariant image restoration algorithms often introduce ringing effects near sharp intensity transitions. A ringing metric to evaluate the quality of images restored using iterative deconvolution algorithms is presented. According to the types of ringing artifacts, two ringing metrics are used to assess the restored images based on the analysis of ringing artifacts. An overall ringing metric is presented based on the two ringing metrics. The experimental results validate that the proposed method performs well over a wide range of restoration image ringing level assessments. Consequently, the proposed model has a good agreement with the ratings from an observer in subjective experiments.
An iterative blind deconvolution algorithm for degrade image is presented in this paper. The algorithm includes two steps, namely, the estimation of the point spread function of degrade image and the restoration using estimated point spread function. Two different Hopfield neural networks are built for realizing the two steps. An iterative procedure is used to control the restoration process. The simulation results indicate that the method is effective for blind deconvolution with high convergence speed.