Summary of Noisediffusion: Correcting Noise For Image Interpolation with Diffusion Models Beyond Spherical Linear Interpolation, by Pengfei Zheng et al.
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation
by PengFei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Image interpolation using diffusion models has shown promising results in generating fresh and interesting images. However, existing methods primarily focus on spherical linear interpolation, which encodes images into noise space for denoising. This approach faces challenges when interpolating natural images not generated by diffusion models, restricting its practical applicability. Our proposed novel approach, NoiseDiffusion, addresses these challenges by correcting noise through subtle Gaussian noise injection and constraining extreme values. By promoting noise validity, NoiseDiffusion mitigates image artifacts but may lead to a reduction in signal-to-noise ratio, causing information loss. To overcome this, NoiseDiffusion performs interpolation within noisy images and injects raw images into these counterparts, enabling accurate interpolation of natural images without artifacts or information loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create new and interesting images by changing old ones. Right now, the best way to do this is by using a special kind of math called diffusion models. However, when we try to make new images from real-world pictures, it doesn’t work as well. The problem is that the noise in the original image messes things up. To fix this, our team came up with a new idea called NoiseDiffusion. It works by making small adjustments to the noise so that it’s more predictable and then using that noise to create new images. This way, we can make new images from real-world pictures without losing any important details. |
Keywords
» Artificial intelligence » Diffusion
