Fast Detection of Curved Edges at Low SNR

TL;DR

This paper introduces a fast multiscale algorithm for detecting curved edges in noisy images, achieving high accuracy and efficiency where existing methods are slow and less effective.

Contribution

A novel multiscale approach that efficiently detects curved edges in high-noise images with near-linear runtime, outperforming previous methods in speed and accuracy.

Findings

Algorithm is orders of magnitude faster than previous methods.

Detects faint curved edges reliably in high noise conditions.

Achieves comparable or better edge detection quality.

Abstract

Detecting edges is a fundamental problem in computer vision with many applications, some involving very noisy images. While most edge detection methods are fast, they perform well only on relatively clean images. Indeed, edges in such images can be reliably detected using only local filters. Detecting faint edges under high levels of noise cannot be done locally at the individual pixel level, and requires more sophisticated global processing. Unfortunately, existing methods that achieve this goal are quite slow. In this paper we develop a novel multiscale method to detect curved edges in noisy images. While our algorithm searches for edges over a huge set of candidate curves, it does so in a practical runtime, nearly linear in the total number of image pixels. As we demonstrate experimentally, our algorithm is orders of magnitude faster than previous methods designed to deal with high noise levels. Nevertheless, it obtains comparable, if not better, edge detection quality on a variety of challenging noisy images.