- The paper introduces a progressive training scheme and facial attention loss to enhance facial feature restoration from low-resolution images.
- It employs a distilled Face Alignment Network to generate landmark heatmaps that prioritize critical facial details during the restoration process.
- Evaluated on datasets like CelebA and AFLW, the method outperforms baselines in PSNR, SSIM, and perceptual quality tests.
Progressive Face Super-Resolution via Attention to Facial Landmark
This paper presents a method for face super-resolution (SR) that employs a progressive training scheme and incorporates a novel facial attention loss to enhance the details of facial features in super-resolved images. The authors address the challenge inherent to face SR, which is the accurate restoration of facial details from low-resolution (LR) images without distortion.
The proposed method adopts a progressive training strategy, which iteratively increases the resolution of the output images through successive training steps. This technique is advantageous for producing photo-realistic outputs and stabilizing the training process, an approach that aligns with trends in natural image SR domains but is novel in its application to face SR. In each training step, both the generator and the discriminator networks are gradually expanded, allowing the model to handle resolution increments progressively.
Key to this approach is the introduction of a facial attention loss, designed to focus the model's learning on the finer details of facial landmarks. This loss function multiplies the pixel difference between predicted and target images by facial heatmap values, thereby assigning more importance to pixel discrepancies in areas surrounding facial features. This attention mechanism relies on a pre-trained Face Alignment Network (FAN), from which a compressed version is distilled to suit the SR task and reduce computational overhead. The distilled FAN provides landmark heatmaps that guide the restoration process effectively by highlighting salient regions on the face.
The performance of the proposed method was evaluated using datasets such as CelebA and AFLW, both in aligned and unaligned formats, demonstrating superiority over contemporary SR approaches. The method was assessed with metrics including PSNR, SSIM, and MS-SSIM, alongside a Mean-Opinion-Score (MOS) test to gauge perceptual quality. On these metrics, the approach consistently outperformed existing methods, indicating the efficacy of the progressive training and attention loss in maintaining the image quality and perceptual fidelity of facial features.
The theoretical implications of the research suggest enhanced facial detail retention through strategic loss weighting and progressive network design. Practically, the authors propose that the method can serve effectively in applications requiring detailed facial reconstructions, such as digital facial reconstruction or security systems.
Future research should explore different methods for generating landmark heatmaps that further enhance SR performance. Moreover, the approach holds promise for adaptation beyond facial SR, suggesting potential applications in other domains requiring precise detail restoration, such as medical imaging and satellite photograph enhancement. The strategy provides a framework for super-resolution tasks where precise detail retention is crucial, leveraging progressive training and attention mechanisms as a robust approach to enhancing image fidelity.