  Following the performance breakthrough of denoising networks, improvements
have come chiefly through novel architecture designs and increased depth. While
novel denoising networks were designed for real images coming from different
distributions, or for specific applications, comparatively small improvement
was achieved on Gaussian denoising. The denoising solutions suffer from
epistemic uncertainty that can limit further advancements. This uncertainty is
traditionally mitigated through different ensemble approaches. However, such
ensembles are prohibitively costly with deep networks, which are already large
in size.
  Our work focuses on pushing the performance limits of state-of-the-art
methods on Gaussian denoising. We propose a model-agnostic approach for
reducing epistemic uncertainty while using only a single pretrained network. We
achieve this by tapping into the epistemic uncertainty through augmented and
frequency-manipulated images to obtain denoised images with varying error. We
propose an ensemble method with two decoupled attention paths, over the pixel
domain and over that of our different manipulations, to learn the final fusion.
Our results significantly improve over the state-of-the-art baselines and
across varying noise levels.
