  Image interpolation based on diffusion models is promising in creating fresh
and interesting images. Advanced interpolation methods mainly focus on
spherical linear interpolation, where images are encoded into the noise space
and then interpolated for denoising to images. However, existing methods face
challenges in effectively interpolating natural images (not generated by
diffusion models), thereby restricting their practical applicability. Our
experimental investigations reveal that these challenges stem from the
invalidity of the encoding noise, which may no longer obey the expected noise
distribution, e.g., a normal distribution. To address these challenges, we
propose a novel approach to correct noise for image interpolation,
NoiseDiffusion. Specifically, NoiseDiffusion approaches the invalid noise to
the expected distribution by introducing subtle Gaussian noise and introduces a
constraint to suppress noise with extreme values. In this context, promoting
noise validity contributes to mitigating image artifacts, but the constraint
and introduced exogenous noise typically lead to a reduction in signal-to-noise
ratio, i.e., loss of original image information. Hence, NoiseDiffusion performs
interpolation within the noisy image space and injects raw images into these
noisy counterparts to address the challenge of information loss. Consequently,
NoiseDiffusion enables us to interpolate natural images without causing
artifacts or information loss, thus achieving the best interpolation results.
