  Diffusion models have demonstrated remarkable efficacy in generating
high-quality samples. Existing diffusion-based image restoration algorithms
exploit pre-trained diffusion models to leverage data priors, yet they still
preserve elements inherited from the unconditional generation paradigm. These
strategies initiate the denoising process with pure white noise and incorporate
random noise at each generative step, leading to over-smoothed results. In this
paper, we present a refined paradigm for diffusion-based image restoration.
Specifically, we opt for a sample consistent with the measurement identity at
each generative step, exploiting the sampling selection as an avenue for output
stability and enhancement. The number of candidate samples used for selection
is adaptively determined based on the signal-to-noise ratio of the timestep.
Additionally, we start the restoration process with an initialization combined
with the measurement signal, providing supplementary information to better
align the generative process. Extensive experimental results and analyses
validate that our proposed method significantly enhances image restoration
performance while consuming negligible additional computational resources.
