Attention-Aware Face Hallucination via Deep Reinforcement Learning (1708.03132v1)

Abstract: Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations.

• The paper introduces Attention-FH, a deep reinforcement learning framework for face hallucination that sequentially enhances faces by focusing on attended patches, integrating a recurrent policy network with a local enhancement network.
• Experiments on BioID and LFW datasets demonstrate Attention-FH consistently outperforms state-of-the-art methods across PSNR, SSIM, and FSIM metrics by prioritizing high-frequency detail regions.
• The framework's adaptive approach improves performance in applications like face recognition and alignment under challenging conditions, offering a novel RL application in image super-resolution.

An Expert Review of "Attention-Aware Face Hallucination via Deep Reinforcement Learning"

In their paper, "Attention-Aware Face Hallucination via Deep Reinforcement Learning," Qingxing Cao et al. present an innovative framework for face hallucination, conceptualized as a domain-specific super-resolution problem aiming at generating high-resolution (HR) faces from low-resolution (LR) images. This framework, referred to as Attention-FH, leverages deep reinforcement learning (RL) to optimize the enhancement process sequentially, focusing on attended patches to fully harness the global contextual interdependencies of facial features.

Unlike traditional methods that typically execute patch-to-patch mappings with minimal dependence on global context due to their limited perspective on individual patches, the Attention-FH framework innovatively integrates a recurrent policy network with a local enhancement network. Such an overview enables sequential and adaptive facial enhancement through a Markov decision process, thus preserving the global integrity of facial structures. This approach, inspired by human visual perception dynamics, positions Attention-FH to outperform existing methodologies.

The proposed framework comprises two key components: the recurrent policy network and the local enhancement network. The former determines optimal facial regions for enhancement through dynamic policy updates influenced by prior outcomes. In tandem, the local enhancement network performs the actual super-resolution enhancement on the selected regions, iteratively refining the facial image by replacing the LR patches with their HR counterparts. Importantly, the recurrent policy network and the local enhancement network are jointly trained to maximize a global reward—a metric assessed by the face hallucination quality—which aligns with the ultimate goal of fostering holistic improvements in facial detail recovery.

Noteworthy results are presented through exhaustive experimentation on standard datasets such as BioID and LFW, where Attention-FH consistently surpasses state-of-the-art methods across various evaluation metrics like PSNR, SSIM, and FSIM. This reinforces the claimed superiority of the proposed sequential attention mechanism, which systematically prioritizes high-frequency detail regions in its enhancement protocol.

From a practical standpoint, the implications of this research are significant. The framework's adaptive approach allows it to fine-tune enhancement paths according to distinct facial characteristics, managing variations in pose, illumination, and blurriness with refined precision. In real-world applications, such capabilities translate to improved performance in areas like face recognition and alignment, especially under suboptimal conditions. On the theoretical front, Attention-FH presents a novel RL application within image super-resolution, contributing insights into how sequential decision-making frameworks can enhance vision tasks through strategic region prioritization.

Looking ahead, there lies potential in expanding the Attention-FH framework's generality beyond specific low-level vision tasks, possibly incorporating variations that could address a broader spectrum of image resolution challenges across different object categories and scenes. Additionally, the successful realization of this system ushers in further exploration into reinforcement learning's broader applicability in image enhancement tasks.

In conclusion, the work by Cao et al. offers meaningful advancements to the face hallucination domain by emphasizing the importance of attention and dynamic policy formulation enabled through reinforcement learning. The introduction of a framework proficient in harnessing global facial interdependencies marks a step forward for practical implementations in computer vision, with lessons applicable to generalized image processing contexts.