Markovian models for one dimensional structure estimation on heavily noisy imagery

TL;DR

This paper introduces a novel Markovian approach for one-dimensional structure estimation in heavily noisy imagery, transforming edge detection into a binary segmentation problem in the wavelet domain using Hidden Markov Models.

Contribution

It proposes a new method that leverages 1D Hidden Markov Models in the wavelet domain for accurate structure detection in noisy images, especially SAR images, improving over traditional CFAR detectors.

Findings

Effective detection of structures in SAR images with various polarizations and textures.

Improved edge detection accuracy demonstrated on simulated and real images.

Method provides a reliable starting point for active contour segmentation.

Abstract

Radar (SAR) images often exhibit profound appearance variations due to a variety of factors including clutter noise produced by the coherent nature of the illumination. Ultrasound images and infrared images have similar cluttered appearance, that make 1 dimensional structures, as edges and object boundaries difficult to locate. Structure information is usually extracted in two steps: first, building and edge strength mask classifying pixels as edge points by hypothesis testing, and secondly estimating from that mask, pixel wide connected edges. With constant false alarm rate (CFAR) edge strength detectors for speckle clutter, the image needs to be scanned by a sliding window composed of several differently oriented splitting sub-windows. The accuracy of edge location for these ratio detectors depends strongly on the orientation of the sub-windows. In this work we propose to transform the edge strength detection problem into a binary segmentation problem in the undecimated wavelet domain, solvable using parallel 1d Hidden Markov Models. For general dependency models, exact estimation of the state map becomes computationally complex, but in our model, exact MAP is feasible. The effectiveness of our approach is demonstrated on simulated noisy real-life natural images with available ground truth, while the strength of our output edge map is measured with Pratt's, Baddeley an Kappa proficiency measures. Finally, analysis and experiments on three different types of SAR images, with different polarizations, resolutions and textures, illustrate that the proposed method can detect structure on SAR images effectively, providing a very good start point for active contour methods.