A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise

TLDR

Standard image enhancement techniques for noncoherent optical images do not work on radar images because radar imaging is coherent. The paper derives a radar imaging model and introduces a method for smoothing noisy radar images. Using the model, the authors formulate an optimum minimum‑MSE filter that is made adaptive by locally estimating parameters to preserve edges while smoothing homogeneous areas. The model demonstrates that radar images contain multiplicative noise, and the resulting adaptive filter is computationally efficient, easily implemented in the spatial domain, and outperforms standard filters in both qualitative and quantitative tests.

Abstract

Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.