Blind Face Restoration via Pseudo-Target Diffusion Fine-Tuning
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Solution Overview
Problem
Existing blind image restoration techniques face challenges in maintaining image fidelity and quality, especially in unsupervised scenarios, due to the need for large paired training datasets and computationally expensive diffusion models, which struggle with out-of-distribution degradations and long inference times.
Innovation Solution
A two-phase training process involving pre-training on synthetic data and fine-tuning with pseudo-target images generated using a denoising diffusion model, where low-frequency content is constrained to preserve structural information, allowing unsupervised training without ground-truth images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised learning with paired training datasets is used for blind image restoration, then the model achieves acceptable performance on training-aligned degradations, but produces severe artifacts on out-of-distribution degradations and requires large paired datasets that are difficult to obtain
Solution Approach 1:
The method performs pre-training on synthetic degraded images before fine-tuning on real degraded images. This preliminary action on synthetic data prepares the model to handle various degradation types, improving its ability to generalize to out-of-distribution degradations while maintaining restoration performance.
Solution Approach 2:
The method introduces a denoising diffusion model as an intermediary to generate pseudo-target images from initial restored images. These pseudo-target images serve as a bridge between the initial restoration and ground truth, enabling unsupervised fine-tuning without requiring actual paired real degraded images.
2Reliability
If pre-trained diffusion models are used for zero-shot image restoration, then the model leverages powerful natural image manifold modeling, but requires sampling process during inference resulting in significant computational cost and extremely slow runtime
Solution Approach 1:
The method performs unsupervised fine-tuning on real degraded images before inference, adapting the pre-trained diffusion model to the specific degradation patterns in the target domain. This preliminary adaptation improves restoration quality for the specific application while maintaining the fast inference capability of the fine-tuned model.
Solution Approach 2:
The method extracts and removes the computationally expensive sampling process from the inference stage by using supervised fine-tuning with pseudo-target images. The fine-tuned model can then perform restoration with a single forward pass, extracting the essential restoration capability while eliminating the slow sampling process.
3Ease of manufacture
If unsupervised training without ground-truth images is performed, then the method avoids the need for large paired datasets, but lacks direct feedback for optimizing restoration fidelity
Solution Approach 1:
The method uses a denoising diffusion model as an intermediary to generate pseudo-target images that serve as surrogate ground truth. These pseudo-target images provide the necessary feedback signal for optimizing restoration fidelity during fine-tuning, while the training remains unsupervised as no actual ground truth images are required.
Solution Approach 2:
The method enables the training process to be self-sufficient by generating its own training targets from the restored images themselves. The denoising diffusion model processes the initial restored images to create pseudo-targets, allowing the system to train without external ground truth data while still achieving fidelity optimization.
Data Source
AI summary
Methods, systems, and apparatuses for training an image restoration model, including: performing pre-training on the image restoration model based on a synthetic training dataset to obtain a pre-trained image restoration model; providing a plurality of real degraded images as input to the pre-trained image restoration model to obtain a plurality of initial restored images; generating a plurality of pseudo-target images by providing the plurality of initial restored images as input to a denoising diffusion model; calculating a training loss corresponding to the plurality of initial restored images and the plurality of pseudo-target images; and modifying at least one parameter of the pre-trained image restoration model based on the training loss to obtain a trained image restoration model.