Towards Robust Blind Face Restoration with Codebook Lookup Transformer (2206.11253v2)

Abstract: Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to 1) improve the mapping from degraded inputs to desired outputs, or 2) complement high-quality details lost in the inputs. In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces. Under this paradigm, we propose a Transformer-based prediction network, named CodeFormer, to model the global composition and context of the low-quality faces for code prediction, enabling the discovery of natural faces that closely approximate the target faces even when the inputs are severely degraded. To enhance the adaptiveness for different degradation, we also propose a controllable feature transformation module that allows a flexible trade-off between fidelity and quality. Thanks to the expressive codebook prior and global modeling, CodeFormer outperforms the state of the arts in both quality and fidelity, showing superior robustness to degradation. Extensive experimental results on synthetic and real-world datasets verify the effectiveness of our method.

• The paper presents CodeFormer, a novel blind face restoration method treating it as a code prediction task using a discrete codebook and a Transformer network.
• CodeFormer demonstrates superior performance over existing methods on synthetic and real-world datasets, showing robustness to severe degradation while maintaining efficiency.
• This approach has significant implications for practical applications requiring high-quality face images and offers a more stable restoration outcome compared to continuous generative models.

An Analysis of Blind Face Restoration: Codebook Lookup Transformer Approach

The paper presents a novel approach to blind face restoration, leveraging a discrete codebook coupled with a Transformer network, named CodeFormer. Blind face restoration involves reconstructing high-quality images from degraded input images, with unknown types and levels of degradation. Traditional approaches in this field have faced challenges due to the ill-posed nature of the problem—where multiple plausible high-quality outputs can exist for a single degraded input.

Methodology

The authors reposition blind face restoration as a code prediction task, by utilizing a discrete codebook to mitigate uncertainty surrounding the restoration mapping. The codebook is constructed via a vector-quantized autoencoder, compressing the face image to a smaller, finite space. This is a departure from generative prior methods that navigate a continuous latent space, often suffering fidelity issues in severely degraded inputs.

Key to this framework is the CodeFormer, a Transformer network responsible for predicting the sequence of code tokens from low-quality input features. This network captures the global composition of facial elements, surpassing local restoration approaches which struggle to recover fine details amidst heavy degradation. Additionally, a controllable feature transformation module provides the flexibility to balance fidelity and image quality across varying levels of degradation.

Experimental Insights

Significantly, CodeFormer demonstrates superior performance across synthetic datasets, outperforming existing methods like DFDNet, PSFRGAN, and GFP-GAN in metrics such as LPIPS, FID, and identity similarity (IDS). On real-world datasets, it showcases robustness to severe degradation, reinforcing its practical applicability. CodeFormer’s efficiency is reflected in its comparable inference speed to state-of-the-art networks, enhancing its feasibility for real-time applications.

Further ablation studies highlight the importance of each component, the utility of the codebook, and the efficacy of the Transformer for code prediction. For instance, the omission of the codebook or switching to CNN-based predictions markedly reduced restoration quality, underscoring the advantage of a discrete codebook space and global modeling of the Transformer.

Implications and Future Directions

The research presented has significant implications in practical applications where high-quality facial images are crucial, such as security and imaging in adverse conditions. By reducing reliance on continuous deep generative models and emphasizing discrete components, the framework aligns more closely with stable restoration outcomes.

Future developments could explore enhancing the expressiveness of the codebook through multi-scale learning or further optimizing Transformer layers to address limitations observed in certain visual scenarios. Additionally, the potential adaptations to broader restoration tasks, including non-facial imagery and color correction, hint at the generalizability of this approach across digital image restoration.

In essence, this paper introduces a robust model for blind face restoration, alleviating key challenges faced by predecessor methods through a strategically designed code prediction framework, leading to high-quality and consistently reliable restoration outcomes.