Diffusion Image Processing With Tiered Sampling Step Sharing
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Solution Overview
Problem
Diffusion-based image processing methods, such as Denoising Diffusion Probabilistic Models (DDPMs), are computationally intensive, leading to significant inference time hurdles that hinder their application in clinical workflows, particularly when processing multiple images.
Innovation Solution
A tiered sampling step sharing method is employed, where a diffusion-based probabilistic model is divided into multiple tiers, with early tiers sharing denoising steps among groups of images and later tiers processing individually to balance computational efficiency and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If diffusion-based probabilistic models are used for image denoising, then image quality is improved, but computational time and processing speed deteriorate
Solution Approach 1:
The patent divides the denoising process into two distinct phases: a shared initial phase where multiple images undergo common denoising steps, and individual subsequent phases where each image is processed separately. This segmentation allows computational resources to be efficiently allocated, reducing redundant processing while maintaining the quality benefits of diffusion models.
Solution Approach 2:
The patent performs preliminary denoising on a representative image or set of representative images before processing individual images. This preliminary action establishes a foundation that can be leveraged across multiple images, reducing the total computational burden while preserving image quality through the subsequent individual processing phases.
2Manufacturing precision
If diffusion-based probabilistic models are used for image denoising, then image quality is improved, but computational complexity and resource consumption worsen
Solution Approach 1:
The patent merges the processing of multiple images by identifying and executing common denoising steps once for a group of images rather than independently for each image. This merging reduces computational complexity and resource consumption while maintaining image quality through the subsequent individual processing phases that preserve image-specific details.
3Manufacturing precision
If multiple images are processed independently using diffusion models, then image quality is maintained, but processing time and computational cost increase
Solution Approach 1:
The patent segments the processing workflow into batch-level shared operations and individual image operations. This segmentation enables parallel processing of multiple images during the shared phase, significantly improving processing throughput while maintaining image quality through the subsequent individual processing phases.
Solution Approach 2:
The patent performs preliminary denoising operations on representative images that can serve multiple images in a batch. This preliminary action increases processing throughput by avoiding redundant computations across similar images while preserving quality through the subsequent individual processing steps.
Data Source
AI summary
A method and apparatus for performing diffusion-based image processing are described. The method includes: obtaining a DDPM trained to restore a target image from noise over T sampling steps; dividing the T sampling steps into M tiers; and processing the plurality of input images in a tier-by-tier manner using the obtained DDPM, to generate a plurality of processed images. In each tier of a first M−1 tiers, the processing further comprises: grouping the plurality of input images into one or more groups, and over a sampling step within the tier, for each group, performing shared diffusion-based image processing on a representative image of the group, so as to generate a representative intermediate image, which is used as a starting point in a subsequent tier. In a last tier of the M tiers, the processing further comprises: over a sampling step, performing diffusion-based image processing independently with respect to each image.