  Image denoising is a well-known and well studied problem, commonly targeting
a minimization of the mean squared error (MSE) between the outcome and the
original image. Unfortunately, especially for severe noise levels, such Minimum
MSE (MMSE) solutions may lead to blurry output images. In this work we propose
a novel stochastic denoising approach that produces viable and high perceptual
quality results, while maintaining a small MSE. Our method employs Langevin
dynamics that relies on a repeated application of any given MMSE denoiser,
obtaining the reconstructed image by effectively sampling from the posterior
distribution. Due to its stochasticity, the proposed algorithm can produce a
variety of high-quality outputs for a given noisy input, all shown to be
legitimate denoising results. In addition, we present an extension of our
algorithm for handling the inpainting problem, recovering missing pixels while
removing noise from partially given data.
