A New Algorithm of Speckle Filtering using Stochastic Distances

TL;DR

This paper introduces a novel speckle filtering algorithm for SAR images based on stochastic distances and goodness-of-fit tests, improving edge preservation and image quality over traditional methods.

Contribution

The paper proposes a new stochastic distance-based filtering method that enhances speckle noise reduction while maintaining image details, compared to existing filters like Lee's.

Findings

Outperforms Lee's filter in preserving edges and details.

Improves image quality metrics such as the Universal Image Quality Index.

Effectively reduces speckle noise in homogeneous regions of SAR images.

Abstract

This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, overlapping samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity SAR data with homogeneous regions using the Gamma model. The proposal is compared with the Lee's filter using a protocol based on Monte Carlo. Among the criteria used to quantify the quality of filters, we employ the equivalent number of looks, line and edge preservation. Moreover, we also assessed the filters by the Universal Image Quality Index and the Pearson's correlation on edges regions.