Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (2103.14006v2)

Abstract: It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. Although several degradation models take additional factors into consideration, such as blur, they are still not effective enough to cover the diverse degradations of real images. To address this issue, this paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations. Specifically, the blur is approximated by two convolutions with isotropic and anisotropic Gaussian kernels; the downsampling is randomly chosen from nearest, bilinear and bicubic interpolations; the noise is synthesized by adding Gaussian noise with different noise levels, adopting JPEG compression with different quality factors, and generating processed camera sensor noise via reverse-forward camera image signal processing (ISP) pipeline model and RAW image noise model. To verify the effectiveness of the new degradation model, we have trained a deep blind ESRGAN super-resolver and then applied it to super-resolve both synthetic and real images with diverse degradations. The experimental results demonstrate that the new degradation model can help to significantly improve the practicability of deep super-resolvers, thus providing a powerful alternative solution for real SISR applications.

• The paper introduces a degradation model that integrates randomized blur, downsampling, and noise to simulate realistic image degradations.
• It employs a multi-stage framework with Gaussian and anisotropic blurs, diverse downsampling methods, and advanced noise models beyond traditional bicubic interpolation.
• Empirical results show significant improvements in deep blind super-resolution performance on both synthetic and real images, indicating practical applicability.

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution: A Review

The paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" proposes an innovative approach to address the limitations of single image super-resolution (SISR) methods when applied to real-world images. The authors focus on the design of a more complex and realistic degradation model, which is crucial for training effective deep blind super-resolution networks.

Summary of the Research

In the field of image super-resolution, the degradation model plays a pivotal role in determining the efficacy of SISR methods. Traditional models, which often assume simplistic forms of degradation such as bicubic interpolation, fail to capture the complexity of real-world degradations, leading to suboptimal performance on real images. This paper introduces a novel degradation framework that encompasses a broader spectrum of degradations by integrating randomly shuffled sequences of blur, downsampling, and noise.

Degradation Model Design

The novel degradation model proposed in this paper includes:

• Blur: Modeled using both isotropic and anisotropic Gaussian kernels applied in potentially multiple stages to simulate different blurring effects present in real images.
• Downsampling: Includes a variety of common downsampling techniques such as nearest neighbor, bilinear, and bicubic interpolations, as well as combinations of these methods.
• Noise: Extends beyond the traditional Gaussian noise to include JPEG compression artifacts and processed camera sensor noise, thereby covering a wider range of noise patterns encountered in practical scenarios.

The use of a randomly shuffled order of these degradation factors allows the model to simulate a vast degradation space, thus enhancing the diversity and realism of training data.

Empirical Results

The researchers validate their model by training a deep blind super-resolver, based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) architecture, using the proposed degradation strategy. The empirical evaluation demonstrates significant improvements in the super-resolver's ability to handle a variety of complex degradations, showcasing its proficiency on both synthetic and real image datasets.

Implications and Future Directions

This work not only highlights the importance of realistic degradation modeling in SISR but also opens avenues for further research in this domain. The robust performance of the proposed degradation model suggests potential applications in improving the generalization of deep learning models trained for image restoration tasks.

Speculation on Future Developments

Looking forward, there are intriguing opportunities to explore adaptive degradation models that dynamically adjust to specific image characteristics or to integrate learning-based approaches for degradation generation. Additionally, this work could influence the development of new evaluation metrics that align better with the perceptual quality of images, especially for non-bicubic types of degradation.

Overall, this paper contributes substantially to the field of computer vision by offering a more practical approach to modeling image degradation, thereby enhancing the applicability of SISR methods in real-world scenarios.