Beyond Linear Spherical Interpolation: Noise Correction for Image Interpolation with Diffusion Models

Image interpolation based on diffusion models is promising in creating fresh and interesting images. Advanced interpolation methods mainly focus on linear spherical interpolation, delivering remarkable success for images generated by diffusion models.However, existing methods struggle with natural images (not generated by diffusion models), limiting practical applications.Our investigation into the interpolation process has unveiled that its shortcomings are rooted in the introduction of inappropriate noise, which may either exceed or fall below the denoising threshold, leading to issues such as image artifacts and information loss in the interpolated images. To address this issue, we initially investigated a direct noise addition method, which improved image quality but introduced unwanted information. Drawing from these findings, we subsequently developed a novel interpolation approach that harnesses the advantages of both techniques. This approach retains the valuable noise with information from the original images while introducing a subtle Gaussian noise to enhance interpolation quality. Moreover, we introduced an innovative constraint on the noise component responsible for generating artifacts and incorporated original image to supplement missing information.These enhancements not only improved the interpolation results for images within the training domain but also extended the capability to interpolate with natural images beyond the training domain, achieving in the best interpolation results to date.