Conditional Diffusion Model for Data Translation
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Solution Overview
Problem
Current image restoration solutions using diffusion models start from Gaussian noise, which lacks structural information, making them inefficient in learning from degraded images, and this issue extends to other data-to-data translation tasks.
Innovation Solution
The method involves computing diffusion bridges between different versions of data to train a conditional diffusion model, allowing it to directly learn and generate data from structurally informative degraded versions, rather than random noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If diffusion models start from Gaussian noise for image restoration, then the model can learn the underlying data distribution, but the structural information from degraded images is lost
Solution Approach 1:
The patent applies preliminary action by pre-processing the degraded image to generate a structurally informative latent representation before feeding it to the diffusion model. This latent code captures essential structural information from the degraded image, which is then used to condition the diffusion process, thereby preserving structural information that would otherwise be lost when starting from random Gaussian noise.
2Loss of information
If diffusion models are trained to sample from degraded distribution, then structural information is preserved, but the model complexity increases
Solution Approach 1:
The patent introduces an intermediary latent code as a mediator between the degraded image and the diffusion model. This latent code serves as a compressed, structurally informative representation that conditions the diffusion process without requiring the model to directly process the full degraded image, thereby managing model complexity while preserving structural information.
3Productivity
If the reverse denoising process starts from Gaussian noise, then the data distribution can be learned, but the training efficiency decreases
Solution Approach 1:
The patent applies preliminary action by pre-computing a structurally informative latent code from the degraded image before the diffusion process. This allows the reverse denoising to start from a more informed state rather than random Gaussian noise, improving training efficiency while maintaining learning accuracy through the conditional guidance provided by the latent code.
Data Source
AI summary
Image restoration generally involves recovering a target clean image from a given image having noise, blurring, or other degraded features. Current image restoration solutions typically include a diffusion model that is trained for image restoration by a forward process that progressively diffuses data to noise, and then by learning in a reverse process to generate the data from the noise. However, the forward process relies on Gaussian noise to diffuse the original data, which has little or no structural information corresponding to the original data versus learning from the degraded image itself which is much more structurally informative compared to the random Gaussian noise. Similar problems also exist for other data-to-data translation tasks. The present disclosure trains a data translation conditional diffusion model from diffusion bridge(s) computed between a first version of the data and a second version of the data, which can yield a model that can provide interpretable generation, sampling efficiency, and reduced processing time.