CNN-augmented SAR image despeckling using modified speckle reducing anisotropic diffusion and discrete wavelet transform

Abstract

Speckle, a multiplicative granular noise, inherently appears in coherent imaging techniques such as synthetic aperture radar (SAR). It deteriorates the visual quality of images, which leads to difficulty in image interpretation for further analysis. Hence, speckle filtering is essential to recover the image details for applications like segmentation and classification. Several despeckling techniques have been developed in the literature, among which anisotropic diffusion (AD) and discrete wavelet transform (DWT) based methods have achieved state-of-the-art despeckling performance. However, AD cannot be employed indefinitely owing to blurring and detail loss. Similarly, DWT produces spurious noise around edges. This paper proposes a high-performance despeckling technique that uses modified speckle reducing anisotropic diffusion as the preprocessing step in a homomorphic architecture. The architecture uses discrete wavelet transform, dynamic weighted adaptive thresholding (DWAT), weighted least squares, and guided filtering to recover the clean image. In addition, to enhance the performance of the despeckling process, a convolutional neural network (CNN) is used as a subsequent processing module to remove residual speckle while preserving the edges. The CNN uses a supervised learning paradigm trained on simulated speckled and clean image pairs to fine-tune the despeckled output. Subjective (visual) and objective evaluations on both simulated and real SAR datasets demonstrate that the proposed hybrid approach achieves robust despeckling performance, particularly excelling in edge preservation, radiometric consistency, and detail reconstruction across varied scene types as compared to the existing methods.