抄録
Relaxation neural network models have been studied to solve basic image processing problems such as quantization and intensity-surface interpolation. First, a relaxation neural network model is proposed to solve the multi-level representation problem for a gray-level image in local and parallel computations. This network iteratively minimizes the energy function defined by the local error in neighboring picture elements and generates high-quality multi-level images depending on local features. Next the applicability of the relaxation network model to intensity-surface interpolation of a gray-level image is studied from sparsely and irregularly sampled data. A relaxation network model is proposed to interpolate the missing gray levels in parallel, thus minimizing the energy function consisting of a membrane and thin plate. This method is effective for intensity-surface interpolation while preserving discontinuities of the image.
