SAR image despeckle with CNN using a novel logarithmic discrete cosine transform-based loss

Official URL: https://dx.doi.org/10.1109/JSTARS.2024.3450429

The coherent nature of imaging in synthetic aperture radar (SAR) inevitably gives rise to speckle noise, a challenge exacerbated by the constrained bandwidth and limited look angles. Among the despeckling algorithms, convolutional neural networks (CNNs) have dominated the forefront of SAR image despeckling, showcasing state-of-the-art performance. CNN-based SAR image despeckling methods excel in learning complex features in an image, while they often struggle with a tendency to induce blurring, leading to a loss of texture information in SAR imagery. We hypothesize that this is mainly caused by the inappropriate use of generic loss functions for despeckling that tend to yield a washed-out blurring effect or introduce artifacts during the denoising process. In this article, we propose a new loss function for an existing CNN architecture that is designed to reduce washed-out blurring effects and is capable of enhancing edges and small-scale features while suppressing noise. The loss function we propose is based on the logarithm of discrete cosine transform images, with a perspective to maintain a nuanced equilibrium between high- and low-energy features in an image, meanwhile maintaining acceptable noise suppression. In comparison to established loss functions applied in SAR image despeckling, experimental results show that the CNNs trained with the proposed loss function not only enhance multiple objective metrics but also exhibit considerable advantages in terms of visual effects.