Edge-Informed Single Image Super-Resolution (1909.05305v1)

Abstract: The recent increase in the extensive use of digital imaging technologies has brought with it a simultaneous demand for higher-resolution images. We develop a novel edge-informed approach to single image super-resolution (SISR). The SISR problem is reformulated as an image inpainting task. We use a two-stage inpainting model as a baseline for super-resolution and show its effectiveness for different scale factors (x2, x4, x8) compared to basic interpolation schemes. This model is trained using a joint optimization of image contents (texture and color) and structures (edges). Quantitative and qualitative comparisons are included and the proposed model is compared with current state-of-the-art techniques. We show that our method of decoupling structure and texture reconstruction improves the quality of the final reconstructed high-resolution image. Code and models available at: https://github.com/knazeri/edge-informed-sisr

• The paper reformulates Single Image Super-Resolution as an image inpainting task, leveraging edge-informed priors to better preserve structural fidelity.
• Their two-stage model uses adversarial networks for sequential edge and texture reconstruction, achieving superior perceptual image quality, especially around edges, compared to state-of-the-art models.
• This edge-informed approach offers potential for applications like medical imaging and surveillance where texture precision is key, suggesting future research into more integrated training paradigms.

Edge-Informed Single Image Super-Resolution

The paper "Edge-Informed Single Image Super-Resolution" introduces a novel approach to addressing the challenges inherent in single image super-resolution (SISR), a domain where recovering high-resolution (HR) images from their low-resolution (LR) counterparts is often confronted with unavoidable ambiguities due to the inherent ill-posed nature of the problem. The authors propose reformulating the SISR challenge as an image inpainting task, utilizing a two-stage model to distinguish structural from textural components of image data. This method leverages a joint optimization strategy for edge and texture reconstruction, with a clear emphasis on preserving structural fidelity during upscaling processes.

Methodological Insights

The crux of the paper’s methodology involves redefining the missing pixel information within an LR image as an analogous problem faced during image inpainting, where missing regions in an image are reconstructed convincingly. By integrating edge-informed priors during super-resolution, the approach aims to mitigate blurring effects typically observed around object boundaries in HR reconstructions when relying solely on pixel-wise loss functions. The architecture proposed encompasses two adversarial networks: one dedicated to edge enhancement and another focused on completing the HR image. These networks operate sequentially to extrapolate and integrate edge information, significantly enhancing the fidelity of the results.

Experimental Results

The authors offer rigorous quantitative and qualitative analysis to support the efficacy of their method. The performance is benchmarked against several state-of-the-art SISR models using datasets such as Set5, Set14, BSD100, and Celeb-HQ. Metrics including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are employed for evaluation. Despite achieving lower PSNR in comparison to existing models, their edge-informed approach exhibits superior perceptual quality, maintaining sharpness around edges—a feature crucial for photo-realistic image synthesis.

For scale factors extending to $\times 8$, their approach managed to produce visually appealing results, showcasing resilience in the face of more challenging resolution transformations where ideal HR information is farther obfuscated.

Implications and Future Work

This work provides compelling evidence that incorporating structural cues during SISR can yield perceptually superior image quality, which may be particularly beneficial for practical applications requiring texture precision, such as medical imaging and surveillance. However, the decoupled strategy of edge enhancement and image completion suggests future potential in optimizing their integration to streamline the computational process further. A seamless joint training paradigm could bolster efficiency and amplify results—a prospective area for continued research.

The method positions itself at the nexus of inpainting techniques and super-resolution challenges, offering a pathway to leveraging external structural information to bolster recovery processes. Encouragingly, the paper suggests room for exploration regarding what additional aspects of an image can be harvested and synthesized through learned priors, which might prompt a broader engagement with cross-disciplinary methodologies within AI research. The paper substantiates an innovative direction in enhancing the perceptual realism of digitally reconstructed image data through cutting-edge AI technologies.