AI Data Denoising with Flexible Diffusion Step Adjustment
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Solution Overview
Problem
Current diffusion probabilistic models for data denoising face inefficiencies due to rigid constraints on the number of denoising operations required, leading to high computational overheads and inflexibility in the inference phase, necessitating retraining when different denoising steps are needed.
Innovation Solution
Generate distribution information based on first and second prediction information using feature processing networks, allowing flexible adjustment of denoising operations without retraining, and perform sampling in distribution space to obtain denoised data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the quantity of denoising operations is reduced to improve efficiency, then productivity is improved, but the denoising effect deteriorates
Solution Approach 1:
The patent changes the parameter of noise variance instead of changing the number of denoising operations. By directly adjusting the noise variance parameter in the diffusion model, the system can control the denoising effect without being constrained by a fixed number of operations, thus improving both efficiency and effectiveness.
2Manufacturing precision
If the quantity of denoising operations is increased to improve denoising effect, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
Instead of increasing the number of denoising operations to improve quality, the patent adjusts the noise variance parameter directly. This allows achieving better denoising effects through parameter optimization rather than through increased operational steps, thereby maintaining high productivity.
3Reliability
If the number of denoising operations is fixed during training to ensure consistency, then reliability is improved, but adaptability deteriorates
Solution Approach 1:
The patent transforms the fixed-step denoising process into a parameter-based control mechanism. By using noise variance as the control parameter instead of operation count, the system maintains training-inference consistency while gaining the flexibility to adapt to different denoising requirements without retraining.
Data Source
AI summary
A data denoising method and a related device are provided. According to the method, an artificial intelligence technology may be used to perform denoising on data, and any target denoising operation in at least one denoising operation performed on noisy data includes: generating, based on first prediction information and second prediction information, distribution information corresponding to the target denoising operation, where the first prediction information indicates predicted noise between second noisy data and clean data, the second prediction information indicates a square of the predicted noise between the second noisy data and the clean data or indicates a square of a predicted distance between the first prediction information and actual noise, and the actual noise includes actual noise between the second noisy data and the clean data; and sampling denoised data in distribution space to which the distribution information points.