Speckle Reduction of SAR Images based on Signal Subspace Technique

In this paper, speckle removal from synthetic aperture
radar (SAR) images using subspace-based technique is
proposed. The fundamental principle is to decompose the vector space of the noisy image into signal-plus-noise subspace and the noise subspace. Noise reduction is achieved by removing the noise subspace and estimating the clean image from the remaining image subspace. Linear estimation of the clean image is performed by minimizing image distortion while maintaining the residual noise energy below some given threshold. Since the noise is considered to be additive with subspace technique, a
homomorphic framework is used to convert the multiplicative
speckle noise into additive. The performance of the proposed
approach is tested with simulated images and with real SAR
images, and compared with Lee filter. The results indicated
significant improvements by the proposed technique in terms
of structural similarity index measure (SSIM) and equivalent
number of looks (ENL).