From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution (2210.00752v2)

Abstract: How to design proper training pairs is critical for super-resolving real-world low-quality (LQ) images, which suffers from the difficulties in either acquiring paired ground-truth high-quality (HQ) images or synthesizing photo-realistic degraded LQ observations. Recent works mainly focus on modeling the degradation with handcrafted or estimated degradation parameters, which are however incapable to model complicated real-world degradation types, resulting in limited quality improvement. Notably, LQ face images, which may have the same degradation process as natural images, can be robustly restored with photo-realistic textures by exploiting their strong structural priors. This motivates us to use the real-world LQ face images and their restored HQ counterparts to model the complex real-world degradation (namely ReDegNet), and then transfer it to HQ natural images to synthesize their realistic LQ counterparts. By taking these paired HQ-LQ face images as inputs to explicitly predict the degradation-aware and content-independent representations, we could control the degraded image generation, and subsequently transfer these degradation representations from face to natural images to synthesize the degraded LQ natural images. Experiments show that our ReDegNet can well learn the real degradation process from face images. The restoration network trained with our synthetic pairs performs favorably against SOTAs. More importantly, our method provides a new way to handle the real-world complex scenarios by learning their degradation representations from the facial portions, which can be used to significantly improve the quality of non-facial areas. The source code is available at https://github.com/csxmli2016/ReDegNet.

• The paper introduces ReDegNet, a method that uses structural priors from low-quality face images to model real-world degradations for natural image super-resolution.
• It employs two key components, DegNet and SynNet, to capture and simulate degradation patterns, resulting in improved PSNR and SSIM metrics.
• The approach offers actionable insights for enhancing image restoration systems and sets a promising direction for degradation modeling using inherent image structures.

From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution

The paper "From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution" presents an innovative approach to tackle the challenge of super-resolving real-world low-quality (LQ) images, especially when it is difficult to acquire paired ground-truth high-quality (HQ) images or synthesize realistic degraded LQ observations. The authors propose a method named ReDegNet that leverages the inherent structural priors of LQ face images and their restored HQ counterparts to model real-world complex degradations and transfer these to natural images. This strategy aims to create realistic synthetic LQ training pairs for enhancing super-resolution performance.

The distinguishing feature of this approach is its reliance on the robust restoration capability of LQ face images derived through strong structural priors. This results in photo-realistic textures that can then inform the degradation model for natural images. Here, ReDegNet comprises two main components: DegNet for capturing degradation patterns from face images and SynNet to apply these patterns to generate synthetic LQ natural images. Through a transfer of degradation representations, the approach synthesizes realistic degradation scenarios, informed by the face images, onto natural images.

The efficacy of the model is demonstrated through rigorous experimentation. Results highlight that when trained on these synthetic pairs, the restoration network performs favorably against state-of-the-art methods. In particular, the method shows significant improvements in diverse complex real-world scenarios—especially notable as these scenarios often present intractable challenges due to the complex nature of degradations.

The strong numerical results, especially regarding metrics such as PSNR and SSIM, suggest that the integration of realistic degradation processes leads to superior performance in both typical and complex scenarios, such as halftone image restoration. The approach underscores the efficacy and potential of using face region degradations as generalizable templates for broader image degradation modeling.

The implications of this research are both theoretical and practical. Theoretically, it provides a novel lens to conceptualize degradation modeling, shifting towards a representation-based approach rather than relying solely on handcrafted or estimated degradation parameters. Practically, this method can be used to improve image restoration frameworks significantly, which has applications in image enhancement for photography, film restoration, and potentially various AI-driven imaging and vision applications where image quality is of essence.

Looking ahead, this approach might inspire further exploration into using specific regions of images or leveraging other well-defined image structures to inform degradation modeling in diverse contexts. Future advancements could potentially harness this methodology to other domains beyond imagery, where structural priors can be defined, offering substantial improvements in fields requiring image synthesis and restoration.