Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains (2403.16205v1)

Abstract: This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. This algorithm works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring. The transformation process, from one blurry state to another, leverages unpaired data consisting of sharp and blurry images captured by the target camera device. Learning this blur-to-blur transformation is inherently simpler than direct blur-to-sharp conversion, as it primarily involves modifying blur patterns rather than the intricate task of reconstructing fine image details. The efficacy of the proposed approach has been demonstrated through comprehensive experiments on various benchmarks, where it significantly outperforms state-of-the-art methods both quantitatively and qualitatively. Our code and data are available at https://zero1778.github.io/blur2blur/

• The paper presents Blur2Blur, which converts unknown blur patterns into a predefined known domain to leverage existing deblurring algorithms.
• It uses an adversarial training approach with reconstruction loss to maintain image content during the blur transformation process.
• Experiments demonstrate significant performance gains, with improved PSNR and SSIM metrics over state-of-the-art methods on multiple benchmark datasets.

Leveraging Known Blurs for Effective Image Deblurring: Introducing Blur2Blur

Introduction

Image deblurring represents a crucial step in improving visual quality and efficacy of computational models in various applications. The challenge intensifies when images originate from unspecified sources, exhibiting unknown blurring patterns. Traditional approaches either directly confront the wide range of potential blurs through supervised learning on large datasets or employ models that generalize across different blurs. However, these strategies often stumble when faced with real-world variability in blur patterns, especially when tailored to specific camera devices. This paper proposes a novel framework, Blur2Blur, designed to adapt image deblurring techniques to handle unknown blurs effectively. By converting unknown blurs into a predefined known blur, which can be efficiently processed by an existing deblurring algorithm, this approach significantly outperforms state-of-the-art methods across various benchmarks.

Method Overview

At the heart of Blur2Blur is the recognition of the impracticality of acquiring extensive matched blurry-sharp image pairs for every potential camera and setting. Instead of this labor-intensive requirement, Blur2Blur cleverly uses unpaired sets of blurry and sharp images captured by the target camera. The innovation lies in transforming the existing blurry images into ones exhibiting a specific, known blur pattern. This transformed set of blurry images can then be effectively deblurred using pre-trained models tailored for the known blur.

Blur-to-Blur Translation

The process begins with the identification of an optimal known blur domain and the development of a blur conversion model. This model learns to translate blur patterns from the camera-specific (unknown) domain to the selected known blur domain while preserving the image content. Training this model involves an adversarial setup, where a discriminator distinguishes between real images of the known domain and converted images, alongside a reconstruction loss ensuring content consistency. The success of this conversion allows the use of specialized deblurring algorithms, originally trained on extensive datasets from the known blur domain, to be applied effectively to images from the target camera.

Selecting a Known Blur Domain

A critical decision is the choice of the known blur domain. To ensure the effectiveness of blur translation and subsequent deblurring, the paper advocates for generating the known blur images from sharp images captured by the same camera, augmented by specific blur patterns. This approach ensures that the discriminator focuses on blur characteristics, facilitating accurate blur conversion.

Experimental Validation

Comprehensive experiments demonstrate Blur2Blur's superiority in deblurring images across various datasets, including REDS, GoPro, RSBlur, and RB2V_street. By integrating Blur2Blur with prominent deblurring networks like Restormer and NAFNet, a significant improvement in performance metrics (PSNR and SSIM) is observed. Moreover, qualitative assessments underline the model's ability to retain image content while altering blur patterns to match the known domain, leading to markedly enhanced results post-deblurring.

Implications and Future Directions

The introduction of Blur2Blur marks a significant advancement in the field of image processing, highlighting a pragmatic approach to tackling the challenge of deblurring images from specific cameras or unknown blur domains. This method's efficacy in utilizing existing deblurring networks for previously intractable blurs opens new avenues for research and application. Future work could explore the optimization of the blur conversion process, extending the framework to other forms of image restoration, and adapting it to dynamic video content for a broader range of real-world applications.

Conclusion

Blur2Blur offers a novel and effective strategy for adapting image deblurring algorithms to specific cameras and unknown blurs, surpassing state-of-the-art methods in handling diverse and challenging blur patterns. This approach mitigates the need for extensive dataset collection and model retraining, presenting a flexible solution to one of the persistent challenges in image processing.