Descriptions of physical properties of visible surfaces, such as their distance and the
presence of edges, must be recovered from the primary image data. Computational vision
aims to understand how such descriptions can be obtained from inherently ambiguous and
noisy data. A recent development in this field sees early vision as a set of ill-posed
problems, which can be solved by the use of regularization methods. These lead to
algorithms and parallel analog circuits that can solve 'ill-posed problems' and which are …