Relaxation neural network models are studied to solve such basic image processing problems as binary quantization, effective sampling and interpolation. A relaxation neural network model is proposed to solve the spatial grey level representation problems in local and parallel computations. This network iteratively minimizes the energy function defined by the local error in neighboring picture elements. For effective binary representation depending on local features such as edges, interactions between binary processes and line processes representing discontinuities of the image are introduced. The applicability of the relaxation network model to intensity surface interpolation of the grey level image, from sparsely sampled data selected by fractal-based sampling, is discussed. A relaxation network model is used to interpolate the missing grey levels in parallel, which minimizes the energy function consisting of a membrane and thin plate, while preserving discontinuities of the image. The randomness controlled by the fractal dimension is introduced to the relaxation neural network model for the representation of small grey level changes.
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