Wavelet-Driven Diffusion Model for High-Frequency Image Synthesis
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Solution Overview
Problem
Existing diffusion models struggle to learn and generate high-frequency information, such as textures and detailed patterns, due to the lossy nature of downsampling operations in their U-Net architecture, which results in incomplete image synthesis.
Innovation Solution
Incorporating wavelet transformation layers into the U-Net architecture to reduce and increase image resolution, allowing the model to retain and propagate high-frequency information, thereby enabling the synthesis of images with realistic textures and patterns without altering the training or sampling procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If downsampling operations are used in U-Net architecture, then the model can process images efficiently, but high-frequency information is lost
Solution Approach 1:
The patent applies segmentation by decomposing the image into different frequency components using wavelet transform. Instead of uniformly downsampling the entire image, the wavelet transform separates high-frequency and low-frequency information into different subbands, allowing selective processing that preserves important high-frequency details while still achieving compression and efficient processing.
Solution Approach 2:
The patent changes the parameter representation of the image by transforming it from spatial domain to frequency domain using wavelet coefficients. This parameter transformation allows the model to work with a different representation that explicitly preserves high-frequency information while maintaining computational efficiency through the multi-resolution nature of wavelet decomposition.
2Loss of information
If wavelet transformation layers are incorporated, then high-frequency information is retained, but the model complexity increases
Solution Approach 1:
The patent makes the wavelet transformation layers universal by designing them to serve multiple functions within the U-Net architecture. The same wavelet transform blocks are used in both the encoder and decoder paths, and they perform both feature extraction and resolution management simultaneously. This multi-functionality reduces the need for separate specialized components, thereby limiting the increase in overall model complexity.
3Manufacturing precision
If wavelet transform is used for resolution reduction, then image quality is preserved, but computational cost increases
Solution Approach 1:
The patent applies partial action by selectively applying wavelet transformation only at specific stages of the U-Net architecture where resolution changes are needed, rather than applying it uniformly throughout. The wavelet transform is used strategically in downsampling and upsampling operations, allowing the model to maintain image quality where critical while reducing unnecessary computational overhead in other regions of the network.
Data Source
AI summary
Systems and methods for synthesizing images with increased high-frequency detail are described. Embodiments are configured to identify an input image including a noise level and encode the input image to obtain image features. A diffusion model reduces a resolution of the image features at an intermediate stage of the model using a wavelet transform to obtain reduced image features at a reduced resolution, and generates an output image based on the reduced image features using the diffusion model. In some cases, the output image comprises a version of the input image that has a reduced noise level compared to the noise level of the input image.