Diffusion Image Restoration With Shared Initial Denoising Steps
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Solution Overview
Problem
Deep learning-based image restoration models require a large number of processing steps, which can increase processing time and computational capacity when applied to sequences of images.
Innovation Solution
A method involving a diffusion-based probabilistic model (DDPM) that groups images into subsets and uses an initial representative image for bulk denoising, reducing the number of denoising steps by applying a sequence of denoising steps to the representative image followed by additional steps specific to each image, thereby optimizing processing time and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a deep learning model processes each image in a sequence by repeating the full series of denoising steps, then each image is restored with high quality, but the processing time and computational capacity increase significantly
Solution Approach 1:
The patent divides the denoising process into two distinct phases: a first phase that processes a representative image from each group, and a second phase that processes individual images using the results from the first phase. This segmentation allows the model to avoid repeating the full denoising sequence for every image, thereby reducing processing time while maintaining restoration quality.
Solution Approach 2:
The patent performs preliminary denoising on a representative image from each group in the first phase, generating intermediate results that are then reused for processing individual images in the second phase. This preliminary action eliminates redundant computations and reduces the overall processing time for image sequences.
2Manufacturing precision
If a deep learning model processes each image in a sequence by repeating the full series of denoising steps, then each image is restored with high quality, but the computational capacity required increases significantly
Solution Approach 1:
The patent merges the processing of multiple images by using a representative image from each group that captures common features. The denoising operations performed on the representative image are combined with individual image processing in the second phase, reducing redundant computational operations while maintaining restoration quality.
Solution Approach 2:
The patent performs preliminary denoising on representative images in the first phase, establishing a foundation that reduces the computational burden for individual image processing in the second phase. This preliminary computation eliminates the need to repeat full denoising sequences for every image, thereby reducing overall computational capacity requirements.
3Productivity
If the number of denoising steps is reduced to decrease processing time, then processing efficiency improves, but the image restoration quality may deteriorate
Solution Approach 1:
The patent segments the denoising process into two phases with different step counts: the first phase uses a reduced number of steps (T1) on representative images, while the second phase uses additional steps (T2) on individual images. This segmentation maintains overall restoration quality by ensuring that sufficient denoising steps are applied in the second phase while improving efficiency through the condensed first phase.
Solution Approach 2:
The patent applies different denoising step configurations to different processing stages: fewer steps are applied in the first phase on representative images, while more steps are applied in the second phase on individual images. This local differentiation optimizes the balance between processing efficiency and restoration quality for each stage of the workflow.
Data Source
AI summary
A method of denoising a plurality of input images, including grouping the plurality of input images into a plurality of groups; determining, for each group of the plurality of groups, an initial representative image for the group; performing, for each group, a sequence of T1 denoising sampling steps using a diffusion-based probabilistic model, starting with the initial representative image for the group, to generate a corresponding sequence of representative images for the group, wherein T1 is an integer greater than or equal to 1; and for each input image in each group, performing a second sequence of T2 denoising sampling steps using the model to generate a final image that is a restored image corresponding to the input image, wherein T2 is an integer greater than or equal to 1, and T=T1+T2.