Diffusion Model Super-Resolution via Pre-Degraded Input
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Solution Overview
Problem
Current diffusion models for super-resolution image processing are slow and resource-intensive, often resulting in suboptimal image quality due to convergence issues and the need for extensive iterations.
Innovation Solution
The method involves obtaining an input image, up-scaling it, degrading the up-sampled image using a partial forward diffusion process, combining the up-sampled and degraded images, and then using a diffusion model to generate a high-quality output image based on the combined data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If diffusion models are used for super-resolution image processing, then image quality is improved, but inferencing speed deteriorates and resource consumption increases
Solution Approach 1:
The patent applies preliminary action by performing up-scaling and degradation operations before the diffusion model processing. The input image is first up-scaled to the target resolution, then degraded to create a noisy version that serves as a better starting point for the diffusion model, reducing the iterations needed for convergence
Solution Approach 2:
The patent segments the super-resolution process into distinct stages: (1) up-scaling the input image to target resolution, (2) degrading the up-sampled image to create noisy input, (3) processing through diffusion model for a limited number of iterations, and (4) combining results. This segmentation allows each stage to be optimized independently
2Manufacturing precision
If extensive iterations are performed in diffusion models, then image quality may improve, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent applies partial action by performing fewer diffusion model iterations than traditionally required. The degraded up-sampled image provides sufficient initial structure that allows the diffusion model to converge with fewer iterations (e.g., 1-4 iterations), reducing processing time while maintaining image quality
Solution Approach 2:
The patent rushes through the diffusion process by limiting iterations and using the degraded image as input. This skipping approach bypasses the need for extensive iterative refinement, achieving acceptable convergence faster by leveraging the pre-processed degraded image structure
3Device complexity
If standard diffusion models are used without pre-processing, then the process is simpler, but color and hue shifts occur and results become unstable
Solution Approach 1:
The patent applies preliminary action by performing up-scaling and degradation operations before the diffusion model processing. The input image is first up-scaled to the target resolution, then degraded to create a noisy version that serves as a better starting point for the diffusion model, reducing the iterations needed for convergence
Solution Approach 2:
The degraded up-sampled image acts as an intermediary between the original low-resolution image and the final high-resolution output. This intermediate representation preserves structural information while adding appropriate noise, serving as an optimal input for the diffusion model and preventing color/hue shifts
Data Source
AI summary
A method includes obtaining, using at least one processing device of an electronic device, an input image. The method also includes up-scaling, using the at least one processing device, the input image to generate an up-sampled image. The method further includes degrading, using the at least one processing device, the up-sampled image to generate a degraded up-sampled image. The method also includes combining, using the at least one processing device, the up-sampled image and the degraded up-sampled image to generate combined data. In addition, the method includes generating, using the at least one processing device, an output image based on the combined data, where the output image is generated using a diffusion model.