In various learning-based image restoration tasks, such as image denoising and image super-resolution, the degradation representations were widely used to model the degradation process and handle complicated degradation patterns. However, they are less explored in learning-based image deblurring as blur kernel estimation cannot perform well in real-world challenging cases. We argue that it is particularly necessary for image deblurring to model degradation representations since blurry patterns typically show much larger variations than noisy patterns or high-frequency textures.In this paper, we propose a framework to learn spatially adaptive degradation representations of blurry images. A novel joint image reblurring and deblurring learning process is presented to improve the expressiveness of degradation representations. To make learned degradation representations effective in reblurring and deblurring, we propose a Multi-Scale Degradation Injection Network (MSDI-Net) to integrate them into the neural networks. With the integration, MSDI-Net can handle various and complicated blurry patterns adaptively. Experiments on the GoPro and RealBlur datasets demonstrate that our proposed deblurring framework with the learned degradation representations outperforms state-of-the-art methods with appealing improvements. The code is released at https://github.com/dasongli1/Learning_degradation.

翻译：在各种基于学习的图像恢复任务中,如图像脱色和图像超分辨率,降解图解被广泛用于模拟降解过程和处理复杂的降解模式;然而,在基于学习的图像分流过程中,降解图解被较少探讨,因为在现实世界中,模糊的内核估计无法产生良好的效果;我们认为,图像分流对于模拟降解图解尤为必要,因为模糊图解模式通常比吵闹模式或高频质质谱显示的变异性要大得多。 在本文件中,我们提议了一个框架,以学习模糊图象的空间适应性降解图解。提出了一个新的图像混合和分流学习过程,以提高降解图解的清晰度。为了在重编和分流中有效地进行学习的降解图解,我们提议了一个多度降解图解网络(MSDI-Net),以便将其纳入神经网络。随着整合,MSDI-Net可以处理各种复杂的模糊图解模式的适应性。GoPro和RealBur数据集的实验表明,我们提议的降解图解和变异性图解图解/变变图解模型正在更新。