Model-based neural evaluation and iterative gradient optimization in image restoration and statistical filtering

An optimal model-based neural evaluation algorithm and an iterative gradient optimization algorithm used in image restoration and statistical filtering are presented. The relationship between the two algorithms is studied. We show that under the symmetric positive-definite condition, a condition easily satisfied in restoration and filtering, intra-pixelsequentialprocessing (IPSP) of model-based neuron evaluation is equivalent to the iterative gradient optimization algorithm. We also show that although both methods provide feasible solutions to fast spatial domain implementation of restoration and filtering techniques, the iterative gradient algorithm is in fact more efficient than the IPSP neuron evaluation method. Visual examples are provided to compare the performance of the two approaches.