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We propose DoPAMINE, a new neural network based multiplicative noisedespeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), whichis a recently proposed neural adaptive image denoiser. While the originalN-AIDE was designed for the additive noise case, we show that the sameframework, i.e., adaptively learning a network for pixel-wise affine denoisersby minimizing an unbiased estimate of MSE, can be applied to the multiplicativenoise case as well. Moreover, we derive a double-sided masked CNN architecturewhich can control the variance of the activation values in each layer andconverge fast to high denoising performance during supervised training. In theexperimental results, we show our DoPAMINE possesses high adaptivity viafine-tuning the network parameters based on the given noisy image and achievessignificantly better despeckling results compared to SAR-DRN, astate-of-the-art CNN-based algorithm.

DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling