Speckle Reduction with Adaptive Stack Filters

TL;DR

This paper investigates adaptive stack filters for speckle noise reduction in SAR images, demonstrating improved image quality and classification accuracy through training with selected samples.

Contribution

It introduces a training method with selected samples for estimating optimal Boolean functions in adaptive stack filters for speckle noise reduction.

Findings

Enhanced image quality in speckled SAR images.

Improved classification accuracy after filtering.

Effective use of training with selected samples.

Abstract

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into stacks of binary images according to a set of thresholds. Each binary image is then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be computed by training using a prototype (ideal) image and its corrupted version, leading to optimized filters with respect to a loss function. In this work we propose the use of training with selected samples for the estimation of the optimal Boolean function. We study the performance of adaptive stack filters when they are applied to speckled imagery, in particular to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images. We used SAR images as input, since they are affected by speckle noise that makes classification a difficult task.