SAR image despeckling through convolutional neural networks
This addresses image quality improvement for synthetic aperture radar users, but it is incremental as it applies an existing CNN approach to a specific domain.
The paper tackles SAR image despeckling by using a convolutional neural network with residual learning to estimate and subtract speckle noise, achieving better performance than state-of-the-art methods on synthetic and real data.
In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.