Learning Dual Memory Dictionaries for Blind Face Restoration (2210.08160v1)

Abstract: To improve the performance of blind face restoration, recent works mainly treat the two aspects, i.e., generic and specific restoration, separately. In particular, generic restoration attempts to restore the results through general facial structure prior, while on the one hand, cannot generalize to real-world degraded observations due to the limited capability of direct CNNs' mappings in learning blind restoration, and on the other hand, fails to exploit the identity-specific details. On the contrary, specific restoration aims to incorporate the identity features from the reference of the same identity, in which the requirement of proper reference severely limits the application scenarios. Generally, it is a challenging and intractable task to improve the photo-realistic performance of blind restoration and adaptively handle the generic and specific restoration scenarios with a single unified model. Instead of implicitly learning the mapping from a low-quality image to its high-quality counterpart, this paper suggests a DMDNet by explicitly memorizing the generic and specific features through dual dictionaries. First, the generic dictionary learns the general facial priors from high-quality images of any identity, while the specific dictionary stores the identity-belonging features for each person individually. Second, to handle the degraded input with or without specific reference, dictionary transform module is suggested to read the relevant details from the dual dictionaries which are subsequently fused into the input features. Finally, multi-scale dictionaries are leveraged to benefit the coarse-to-fine restoration. Moreover, a new high-quality dataset, termed CelebRef-HQ, is constructed to promote the exploration of specific face restoration in the high-resolution space.

• The paper introduces DMDNet, a novel model that leverages dual memory dictionaries to adaptively restore facial images without predefining degradation parameters.
• It combines generic facial priors with identity-specific features, achieving enhanced photo-realism and identity consistency in restoration.
• Experimental results demonstrate superior performance in PSNR, SSIM, and LPIPS metrics across both synthetic and real-world datasets.

Overview of "Learning Dual Memory Dictionaries for Blind Face Restoration"

The paper "Learning Dual Memory Dictionaries for Blind Face Restoration" introduces a novel approach to address the complex and frequently encountered problem of blind face restoration. This task involves reconstructing high-quality facial images from degraded inputs without predefining the degradation parameters. Such restoration is particularly relevant for applications like recovering images from albums, films, and old photographs.

Methodology

The core contribution of this paper is the Dual Memory Dictionary Network (DMDNet), which employs a dual dictionary framework to enhance the restoration process. Traditional approaches in blind face restoration either focus on generic restoration, which leverages general facial priors, or specific restoration, which relies on identity-belonging features from reference images. These methods, however, face challenges like poor generalization in real-world applications and limited applicability when suitable references are unavailable.

To overcome these challenges, the authors propose a method that combines the strengths of both approaches through dual memory dictionaries:

1. Generic Dictionary: This dictionary memorizes general facial priors from high-quality images across different identities, aiming to provide a broad associative framework beneficial in various scenarios.
2. Specific Dictionary: It stores identity-specific features by utilizing high-quality reference images of the same identity, thus enhancing restoration when particular identity attributes are essential.

The model incorporates a dictionary transform module designed to read relevant details from these dictionaries and integrates them into the low-quality input, facilitating adaptive restoration. Furthermore, by employing multi-scale dictionaries, DMDNet achieves coarse-to-fine restoration, improving overall photo-realism and detail preservation in the output images.

Experimental Results and Implications

The experimental results demonstrate that DMDNet outperforms existing methods, achieving superior performance in both quantitative measures (PSNR, SSIM, LPIPS, FID) and visual quality on synthetic and real-world low-quality images. The proposed network effectively balances photo-realism with identity consistency, contributing significantly to the identity-preserving image restoration field.

A notable aspect of this research is the construction of a new high-resolution face dataset, CelebRef-HQ, designed to facilitate specific face restoration tasks. This dataset could become instrumental in advancing research in high-resolution face restoration.

Future Directions

The introduction of DMDNet opens several future research avenues. The improvement of dictionary optimization techniques could further enhance the adaptability and efficiency of restoration processes. Additionally, extending the methodology to accommodate other image restoration tasks beyond facial images could broaden the applicability of memory dictionary-based approaches.

The integration of more sophisticated identity recognition technologies and exploring the combination of DMDNet with other deep learning techniques, such as GANs, might provide opportunities to push the boundaries of what is achievable in multimedia restoration.

In summary, the paper presents a robust method for blind face restoration by introducing a dual dictionary strategy that effectively combines generic and specific facial features. This work contributes valuable insights and tools to the field, with promising directions for future exploration and applications.