Blind Face Restoration via Deep Multi-scale Component Dictionaries (2008.00418v1)

Abstract: Recent reference-based face restoration methods have received considerable attention due to their great capability in recovering high-frequency details on real low-quality images. However, most of these methods require a high-quality reference image of the same identity, making them only applicable in limited scenes. To address this issue, this paper suggests a deep face dictionary network (termed as DFDNet) to guide the restoration process of degraded observations. To begin with, we use K-means to generate deep dictionaries for perceptually significant face components (\ie, left/right eyes, nose and mouth) from high-quality images. Next, with the degraded input, we match and select the most similar component features from their corresponding dictionaries and transfer the high-quality details to the input via the proposed dictionary feature transfer (DFT) block. In particular, component AdaIN is leveraged to eliminate the style diversity between the input and dictionary features (\eg, illumination), and a confidence score is proposed to adaptively fuse the dictionary feature to the input. Finally, multi-scale dictionaries are adopted in a progressive manner to enable the coarse-to-fine restoration. Experiments show that our proposed method can achieve plausible performance in both quantitative and qualitative evaluation, and more importantly, can generate realistic and promising results on real degraded images without requiring an identity-belonging reference. The source code and models are available at \url{https://github.com/csxmli2016/DFDNet}.

• The paper introduces DFDNet as a novel blind face restoration method that generates deep component dictionaries independent of identity for detailed facial feature recovery.
• It employs a progressive, multi-scale approach with a Dictionary Feature Transfer block using AdaIN and confidence scoring to robustly match degraded features.
• Experimental results show significant improvements in PSNR, SSIM, and LPIPS, demonstrating the method’s effectiveness in restoring realistic textures across challenging scenarios.

Overview of Blind Face Restoration via Deep Multi-scale Component Dictionaries

The paper titled "Blind Face Restoration via Deep Multi-scale Component Dictionaries" addresses the challenges associated with the restoration of low-quality (LQ) face images to high-quality (HQ) outputs without requiring high-fidelity reference images of the same identity. Conventional reference-based methods show proficiency in restoring details when given a specific HQ reference from the same identity. However, these methods face limitations in applicability due to their dependence on identity-specific references, which are not always available or feasible. The proposed methodology, Deep Face Dictionary Network (DFDNet), innovatively circumvents this constraint.

The approach begins by generating deep dictionaries of significant facial components using a large dataset of HQ images. Key facial components such as eyes, nose, and mouth are extracted using K-means clustering from the feature space of a pre-trained model. These dictionaries serve as sources of detailed features which can later be matched with degraded images to aid in the restoration process without the need for reference images from the same identity.

Methodological Advances and Key Contributions

1. Deep Component Dictionaries: The paper introduces the generation of deep component dictionaries that encompass significant facial features independently of a specific identity. The use of the VggFace model facilitates extraction of multi-scale features, ensuring robust reference dictionaries that capture a wide range of facial characteristics across various scales.
2. Dictionary Feature Transfer (DFT) Block: This novel block incorporates component AdaIN (Adaptive Instance Normalization) and a confidence scoring mechanism. AdaIN enhances feature matching by eliminating style variations between the input and dictionary features. The confidence score further refines restoration by weighting dictionary features according to degradation levels of the input.
3. Progressive Restoration: DFDNet employs a multi-scale progressive approach leveraging component dictionaries in a coarse-to-fine manner, which enhances the model's ability to recover detailed features gradually from coarse global structures to finer local textures.
4. Practical Applicability: The model's ability to deliver high-quality results on LQ images without identity-specific references underscores its versatility in practical applications, including archives where such references are absent.

Numerical Results and Performance

The experiments validate the efficacy of DFDNet in both synthetic and real degraded scenarios. The model demonstrates superior performance in quantitative metrics — notably, achieving enhanced PSNR, SSIM, and reduced LPIPS — when compared to existing methods like GFRNet and GWAINet. The qualitative outputs also confirm the reconstruction of realistic textures and fine details which are critical for human faces.

Theoretical and Practical Implications

Theoretically, the paper contributes to the domain by decoupling the reliance on identity-belonging references in face restoration, which expands the boundary of reference-based methods to more generalized applications. Practically, this advancement enables robust restoration in diverse contexts such as film archives, historical document preservation, and general photography enhancement where HQ pre-references don't exist.

Future Developments

The emergence of DFDNet paves the way for future explorations into deep learning-driven enhancement technologies that operate without specific reference dependencies. It suggests potential advancements in real-time applications where rapid and automated face restoration is crucial. The adaptive norm and confidence score elements within the DFT block could be integrated into broader image processing systems requiring similar resynchronization of style and content.

In conclusion, the development of DFDNet signifies a meaningful step in blind face restoration, moving towards reference-independent methodologies that promise scalability and wider applicability in various computational photography and forensic domains while maintaining or even enhancing restoration fidelity.