We describe a new class of neural network aimed at early visual processing; we call it a Neural Analog Diffusion-Enhancement Layer or NADEL. The network consists of two levels which are coupled through nonlinear feedback. The lower level is a two-dimensional diffusion map which accepts binary visual features as input (e.g. edges and points), and spreads activity over larger scales as a function of time. The upper layer is fed the activity from the diffusion layer and serves to locate local maxima in it (an extreme form of contrast enhancement). These local maxima are fed back to the diffusion layer using an on-center/off-surround shunting anatomy. The maxima are also available as output of the network. The network dynamics serves to cluster features on multiple scales as a function of time, and can be used to support a large variety of early visual processing tasks such as: extraction of corners and high curvature points along edge contours, line end detection, filling gaps and completing contour boundaries, generating saccadic eye motion sequences, perceptual grouping on multiple scales, correspondence and path impletion in long-range apparent motion, and building 2-D shape representations that are invariant to location, orientation and scale on the visual field. The NADEL is now being designed for implementation in Analog VLSI wafer-scale circuits.