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SAR Image Denoising in High Dynamic Range with Speckle and Thermal Noise Refinement Modeling

Synthetic Aperture Radar (SAR) images inevitably contain speckle noise. Despeckling SAR images are typically represented as linear forms due to the consistency of denoising network training/inference settings on a linear scale. However, this leads to controversial problems when the linear SAR images are seen with the naked eye: i) restriction of representation for dark areas and ii) excessive expression for bright areas due to the high-intensity range. To overcome these problems, we propose a denoising framework that simultaneously eliminates speckle and thermal noise in the decibel (dB) domain through novel noise modeling. Our noise modeling allows the network to learn in the dB domain of the desired dynamic range, enabling stable end-to-end learning without separate spatial transformations. Our modeling is the first attempt to consider thermal noise. Experimental results show that our method is superior in quantitative and visual performance compared to the existing methods.