Speckle noise filtering based on signal subspace technique

In this paper, a subspace-based technique for speckle noise reduction in images is proposed. The method is based on a linear model obtained by minimizing the energy of image distortion while keeping the energy of the residual noise in each spectral component below some given threshold. Image enhancement is achieved by removing the noise subspace and estimating the clean signal from the remaining signal subspace. The performance of the proposed approach is tested with simulated images and real SAR images, and compared with Lee and homomorphic anisotropic diffusion filters. The results indicate that the proposed technique increases the signal-to-noise ratio (SNR) by 3.6dB to 5.7dB over the noisy image. In addition, the proposed SDC algorithm is better at preserving the fine texture and introduces less blurring effect into the denoised image.