JEDI: Adaptive Stochastic Estimation for Joint Enhancement and Despeckling of Images for SAR

Synthetic aperture radar (SAR) images are degraded by a form of multiplicative noise known as speckle. Current methods for despeckling are limited in that they either do not perform enough noise attenuation, or do not adequately preserve or enhance image detail. We propose a novel adaptive stochastic method for joint enhancement and despecking of images (JEDI) for SAR. The proposed method utilizes an adaptive importance sampling scheme based on local statistics to generate random samples while reducing estimation variance. A Monte Carlo estimate is computed based on the generated samples, wherein the samples are aggregated to form a despeckled and detail-enhanced result. The advantage of JEDI is the ability to efficiently take advantage of information redundancy in speckled images to reduce the effects of speckle while simultaneously enhancing detail visualization. Testing with both simulated and real speckled images shows that JEDI typically outperforms popular despeckling algorithms such as Frost filtering, anisotropic diffusion, median filtering, Gamma-MAP and GenLik in terms of quantitative and qualitative visual quality. On average, JEDI provides a 2-15% improvement in PSNR and a 5-14% improvement in image quality index measures over the tested methods.