Scale space localization, blur, and contour-based image coding

We have recently proposed a scale-adaptive algorithm for reliable edge detection and blur estimation. The algorithm produces a contour code which consists of estimates of position, brightness, contrast and blur for each edge point in the image. Here we address two questions: 1. Can scale adaptation be used to achieve precise localization of blurred edges? 2. How much of the perceptual content of an image is carried by the 1-D contour code? We report an efficient algorithm for subpixel localization, and show that local scale control allows excellent precision even for highly blurred edges. We further show how local scale control can quantitatively account for human visual acuity of blurred edge stimuli. To address the question of perceptual content, we report an algorithm for inverting the contour code to reconstruct an estimate of the original image. While reconstruction based on edge brightness and contrast alone introduces significant artifact, restitution of the local blur signal is shown to produce perceptually accurate reconstructions.