Learning Spatial Attention for Face Super-Resolution (2012.01211v2)

Abstract: General image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods tailored for face images have achieved improved performance by jointly trained with additional task such as face parsing and landmark prediction. However, multi-task learning requires extra manually labeled data. Besides, most of the existing works can only generate relatively low resolution face images (e.g., $128\times128$), and their applications are therefore limited. In this paper, we introduce a novel SPatial Attention Residual Network (SPARNet) built on our newly proposed Face Attention Units (FAUs) for face super-resolution. Specifically, we introduce a spatial attention mechanism to the vanilla residual blocks. This enables the convolutional layers to adaptively bootstrap features related to the key face structures and pay less attention to those less feature-rich regions. This makes the training more effective and efficient as the key face structures only account for a very small portion of the face image. Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e.g., $16\times16$). Quantitative comparisons on various kinds of metrics (including PSNR, SSIM, identity similarity, and landmark detection) demonstrate the superiority of our method over current state-of-the-arts. We further extend SPARNet with multi-scale discriminators, named as SPARNetHD, to produce high resolution results (i.e., $512\times512$). We show that SPARNetHD trained with synthetic data cannot only produce high quality and high resolution outputs for synthetically degraded face images, but also show good generalization ability to real world low quality face images.

• The paper presents SPARNet, which integrates Face Attention Units into residual networks to target and enhance key facial features.
• It extends to SPARNetHD by incorporating multi-scale discriminators, achieving high-quality outputs up to 512×512 pixels.
• Quantitative evaluations show improvements in PSNR, SSIM, and landmark accuracy, demonstrating the model's efficiency and robustness.

Learning Spatial Attention for Face Super-Resolution: An Expert Overview

The paper introduces a novel methodology aimed at advancing face super-resolution (SR) through the deployment of a spatial attention mechanism integrated within the architecture of deep neural networks. As the task of face SR requires detailed recovery of intricate facial features from low-resolution inputs, traditional methodologies have been somewhat lacking in generating high-quality higher-resolution outputs due to their generic nature. This research proposes a SPatial Attention Residual Network (SPARNet) designed explicitly for this purpose, further extending it to SPARNetHD to address challenges in real-world applications.

Contributions and Methodology

1. SPatial Attention Residual Network (SPARNet): The core novelty of SPARNet lies in the integration of Face Attention Units (FAUs) within traditional residual networks. By embedding spatial attention, the model dynamically emphasizes key facial regions, facilitating the reconstruction of crucial components such as eyes, mouths, and facial contours. This mechanism operates by assigning attention scores to spatial features, enabling efficient and focused learning, which in turn enhances the model’s capability to generate resolutions up to $512\times512$ pixels - a significant leap from the common production limit of $128\times128$.
2. SPARNetHD and Multi-Scale Contexts: The paper extends the basic SPARNet framework to SPARNetHD by employing multi-scale discriminators, similar to Pix2PixHD systems, to further refine output texture detail and realism. This approach achieves superior performance on existing benchmarks while demanding no additional supervised signals like face parsing maps or landmarks used in prior works. Crucially, SPARNetHD displays robustness and adaptability to both synthetic and genuine low-quality images, underscoring its practical applicability.
3. Quantitative Superiority: Through rigorous evaluations on the Helen and UMD datasets, SPARNet demonstrates quantifiable improvements in PSNR, SSIM, and identity similarity metrics. Landmark detection accuracy—a critical indicator of structural fidelity—also favors SPARNet, illustrating its efficacy in preserving essential facial features against comparative baselines such as typical CNNs and systems like FSRNet and URDGN.
4. Flexibility and Efficiency: Attention mechanisms specifically tailored to the face SR task, as opposed to more generalized approaches, bolster SPARNet's performance. The ability of FAUs to intelligently discern and emphasize feature concentrations leads to a markedly more efficient learning process. Such efficiency is evidenced by SPARNet’s superior performance with a relatively modest computational overhead.

Implications and Future Directions

The development of SPARNet and its extension SPARNetHD represents a significant advancement in the field of super-resolution specific to facial images. By enhancing critical facial features through a domain-specific approach, this model not only sets a new benchmark for image quality but also emboldens further exploration into super-resolution tasks with similar critical-structure emphasis.

From a theoretical standpoint, the integration of spatial attention into residual networks opens up potential avenues for further refinement and augmentation by exploring alternative attention mechanisms or hyper-parameter optimizations. Researchers might explore the scalability of this approach to other domains of structured image generation, such as medical imaging or remote sensing.

Practically, as SPARNetHD demonstrates high-quality synthesis from low-quality inputs, its application could be crucial for enhancing legacy or degraded biometric datasets without the need for high-quality original inputs. Moreover, its robustness across synthetic and real-world image inputs signifies a broad spectrum of deployment scenarios in surveillance, historical image restoration, and mobile device-based imaging.

Overall, this work provides a substantial contribution to the domain of face super-resolution, aligning both theoretical innovation and practical applicability toward resolving prevalent issues in high-resolution facial feature generation.