  Image denoising is a fundamental and challenging task in the field of
computer vision. Most supervised denoising methods learn to reconstruct clean
images from noisy inputs, which have intrinsic spectral bias and tend to
produce over-smoothed and blurry images. Recently, researchers have explored
diffusion models to generate high-frequency details in image restoration tasks,
but these models do not guarantee that the generated texture aligns with real
images, leading to undesirable artifacts. To address the trade-off between
visual appeal and fidelity of high-frequency details in denoising tasks, we
propose a novel approach called the Reconstruct-and-Generate Diffusion Model
(RnG). Our method leverages a reconstructive denoising network to recover the
majority of the underlying clean signal, which serves as the initial estimation
for subsequent steps to maintain fidelity. Additionally, it employs a diffusion
algorithm to generate residual high-frequency details, thereby enhancing visual
quality. We further introduce a two-stage training scheme to ensure effective
collaboration between the reconstructive and generative modules of RnG. To
reduce undesirable texture introduced by the diffusion model, we also propose
an adaptive step controller that regulates the number of inverse steps applied
by the diffusion model, allowing control over the level of high-frequency
details added to each patch as well as saving the inference computational cost.
Through our proposed RnG, we achieve a better balance between perception and
distortion. We conducted extensive experiments on both synthetic and real
denoising datasets, validating the superiority of the proposed approach.
