Adaptive large-scale clutter removal from imagery with application to high-resolution sonar imagery

The ability to reliably detect targets having signatures comprised of bright pixels (highlight) and dark pixels (shadow) is challenging when the background texture of the imagery also possesses bright and dark characteristics. This is especially difficult when the background contains large bright and dark areas that can mask target signatures. Detection and classification algorithms would benefit from an adaptive denoising algorithm that would remove or mitigate such background artifacts. This paper presents a Fourier-based denoising algorithm. The large support of the Fourier basis is used to capture and remove large-scale artifacts while leaving the smaller target-size features nearly unchanged. Data-driven soft thresholds allow the algorithm to automatically adapt to changing backgrounds. Preliminary investigations have demonstrated excellent performance. The algorithm is computationally fast and suitable for real-time application. The denoising algorithm is general in nature and can be applied to many types of high-resolution gray-scale imagery; e. g., side-looking sonar and SAR.