Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration (2203.08444v1)

Published 16 Mar 2022 in cs.CV

Abstract: Emerging high-quality face restoration (FR) methods often utilize pre-trained GAN models (\textit{i.e.}, StyleGAN2) as GAN Prior. However, these methods usually struggle to balance realness and fidelity when facing various degradation levels. Besides, there is still a noticeable visual quality gap compared with pre-trained GAN models. In this paper, we propose a novel GAN Prior based degradation-aware feature interpolation network, dubbed Panini-Net, for FR tasks by explicitly learning the abstract representations to distinguish various degradations. Specifically, an unsupervised degradation representation learning (UDRL) strategy is first developed to extract degradation representations (DR) of the input degraded images. Then, a degradation-aware feature interpolation (DAFI) module is proposed to dynamically fuse the two types of informative features (\textit{i.e.}, features from input images and features from GAN Prior) with flexible adaption to various degradations based on DR. Ablation studies reveal the working mechanism of DAFI and its potential for editable FR. Extensive experiments demonstrate that our Panini-Net achieves state-of-the-art performance for multi-degradation face restoration and face super-resolution. The source code is available at https://github.com/jianzhangcs/panini.

Citations (21)

Summary

  • The paper introduces a degradation-aware feature interpolation (DAFI) module that fuses GAN Prior with degraded inputs for improved face restoration.
  • It presents unsupervised degradation representation learning (UDRL) to adaptively handle various degradation levels without labeled data.
  • Panini-Net achieves notable performance gains in restoring multi-degraded faces, with superior FID scores compared to state-of-the-art methods.

Overview of Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration

The paper presents Panini-Net, a novel framework designed to enhance face restoration (FR) tasks through GAN Prior based degradation-aware feature interpolation. The central thesis of the research is to address the limitations of existing GAN Prior methods, particularly in balancing realness and fidelity across various degradation levels. The proposed framework leverages the stylistic prowess of pre-trained GAN models, such as StyleGAN2, to embed high-quality details while concurrently incorporating structural features from the input images to preserve identity consistency.

Key Contributions

  1. Degradation-Aware Feature Interpolation (DAFI): The authors introduce a degradation-aware feature interpolation module that dynamically fuses features from the GAN Prior and the degraded input image. This module is guided by degradation representations extracted through a pre-trained degradation representation encoder (DRE), which is an innovation aimed at improving robustness against different degradation levels.
  2. Unsupervised Degradation Representation Learning (UDRL): A novel approach to extracting degradation representations without supervision is proposed, enabling the network to learn abstract degradation features effectively. This strategy enhances the adaptability of the network to different levels of image degradation.
  3. Panini-Net Framework: The integration of the aforementioned modules into the Panini-Net framework results in a system capable of significant performance improvements in multi-degradation face restoration and face super-resolution tasks. The network's architecture is inspired by the concept of dynamic feature selection and fusion, akin to constructing a sandwich, hence the nomenclature.
  4. Efficiency and Flexibility: The DAFI module is highlighted for its efficient feature fusion, accomplished with a relatively low parameter count, and for its demonstrated flexibility in generating multiple high-quality restorative outputs by adjusting feature fusion parameters.

Numerical Results

Experiments conducted by the authors establish Panini-Net's efficacy, with superior performance metrics in multi-degradation face restoration and significant improvements in FID (Fréchet Inception Distance) scores when compared to other state-of-the-art GAN Prior based methods, such as GFP-GAN and GPEN. The results underscore the network's ability to maintain a higher level of visual quality with realness and identity consistency even under severe degradation conditions.

Implications and Future Directions

Panini-Net's novel approach to handling feature interpolation and degradation representations has clear implications for the field of AI-driven image restoration. Practically, it provides a framework that could significantly enhance applications requiring high fidelity image restoration under varied conditions. Theoretically, it opens avenues for further exploration into more sophisticated feature integration and representation learning strategies.

Looking ahead, potential developments could involve extending the framework's applicability to broader image restoration domains and exploring more complex degradation models. Additionally, the insights from dynamic feature fusion can inspire future studies aimed at optimizing task-specific architectures within the vast landscape of generative models.

In summary, this paper introduces Panini-Net as a progressive step in utilizing GAN Prior for face restoration, providing a robust solution adaptable to varying degradation contexts and contributing to the broader theoretical understanding and practical capabilities in artificial intelligence image processing.