Title: Nonlinear Image Representation via Local Multiscale Orientation 


Authors:  David K. Hammond and Eero P. Simoncelli 


We present a nonlinear image representation based on multiscale local orientation measurements. Specifically, an image is first decomposed using a two-orientation steerable pyramid, a tight-frame representation in which the basis functions are directional derivatives of a radially symmetric blurring operator. The pair of subbands at each scale are thus gradients of progressively blurred copies of the original image. We then discard the magnitude information and retain only the orientation of each gradient vector. We develop a method for reconstructing the original image from this orientation information using an algorithm based on projection onto convex sets, and demonstrate its robustness to quantization.