Blind Image Despeckling Using a Multiscale Attention-Guided Neural Network

Abstract

Coherent imaging systems have been applied in the detection of target of interest, natural resource exploration, ailment diagnosis, etc. However, it is easy to generate speckle-degraded images due to the coherent interference of reflected echoes, restricting these practical applications. Speckle noise is a granular interference that affects the observed reflectivity. It is often modeled as multiplicative noise with a negative exponential distribution. This non-linear property makes despeckling of imaging data an intractable problem. To enhance despeckling performance, we propose to blindly remove speckle noise using an intelligent computing-enabled multi-scale attention-guided neural network (termed MSANN). In particular, we first introduce the logarithmic transformation to convert the multiplicative speckle noise model to an additive version. Our MSANN, essentially a feature pyramid network (FPN), is then exploited to restore degraded images in the logarithmic domain. To enhance the generalization ability of MSANN, a multi-scale feature enhancement attention module (FEAM) is incorporated into MSANN to extract multi-scale features for improving imaging quality. A multi-scale mixed loss function is further presented to increase network robustness during training. The final despeckled images are naturally equivalent to the exponential versions of the output of MSANN. Experimental results have shown that MSANN has the capacity of effectively removing speckle noise while preserving essential structures. It can achieve superior despeckling results in terms of visual quality and quantitative measures.