Learning Blind Pixelwise Affine Image Denoiser With Single Noisy Images

Jaeseok Byun, Taesup Moon

Abstract: Convolutional neural network (CNN)-based denoisers are recently shown to overwhelm the denoising performances of the conventional prior- or optimization-based methods, particularly for the additive white Gaussian noise (AWGN) case. However, those typical CNN-based denoisers had two main limitations: (1) the superb denoising performances for AWGN do not necessarily carry over to the signal-dependent real-noise cases and (2) the clean source images are required as targets for supervised training. Both limitations are critical in practical real-noise denoising setting, since neither the noise characteristics nor the clean source images are readily available for supervised training. While some recent work proposed CNN denoisers that are trained solely with noisy images, they still fall short of the classical baselines, e.g., BM3D, for blind real-noise denoising settings, in which nothing is known about the noise and only noisy images are available for training. In this paper, we propose Blind Pixelwise Affine Image DEnoiser (BP-AIDE), which tackles above two limitations by combining variance stabilizing transformation (VST) technique with a recently developed unbiased estimate of the mean-squared error (MSE) for a pixelwise affine denoiser. As a result, we show BP-AIDE can be blindly trained solely with single real-noise corrupted images and, to the best of our knowledge, first outperforms both CNN-based and conventional denoisers for the same setting, on several real-world noisy image benchmark datasets.