  Image denoising is a classic restoration problem. Yet, current deep learning
methods are subject to the problems of generalization and interpretability. To
mitigate these problems, in this project, we present a framework that is
capable of controllable, confidence-based noise removal. The framework is based
on the fusion between two different denoised images, both derived from the same
noisy input. One of the two is denoised using generic algorithms (e.g.
Gaussian), which make few assumptions on the input images, therefore,
generalize in all scenarios. The other is denoised using deep learning,
performing well on seen datasets. We introduce a set of techniques to fuse the
two components smoothly in the frequency domain. Beyond that, we estimate the
confidence of a deep learning denoiser to allow users to interpret the output,
and provide a fusion strategy that safeguards them against out-of-distribution
inputs. Through experiments, we demonstrate the effectiveness of the proposed
framework in different use cases.
