IEEE Trans Pattern Anal Mach Intell. 2019 Sep;41(9):2112-2130. doi: 10.1109/TPAMI.2018.2855177. Epub 2018 Jul 11.
A fundamental problem in image deblurring is to recover reliably distinct spatial frequencies that have been suppressed by the blur kernel. To tackle this issue, existing image deblurring techniques often rely on generic image priors such as the sparsity of salient features including image gradients and edges. However, these priors only help recover part of the frequency spectrum, such as the frequencies near the high-end. To this end, we pose the following specific questions: (i) Does any image class information offer an advantage over existing generic priors for image quality restoration? (ii) If a class-specific prior exists, how should it be encoded into a deblurring framework to recover attenuated image frequencies? Throughout this work, we devise a class-specific prior based on the band-pass filter responses and incorporate it into a deblurring strategy. More specifically, we show that the subspace of band-pass filtered images and their intensity distributions serve as useful priors for recovering image frequencies that are difficult to recover by generic image priors. We demonstrate that our image deblurring framework, when equipped with the above priors, significantly outperforms many state-of-the-art methods using generic image priors or class-specific exemplars.
图像去模糊的一个基本问题是可靠地恢复被模糊核抑制的空间频率。为了解决这个问题，现有的图像去模糊技术通常依赖于通用的图像先验，例如显著特征（包括图像梯度和边缘）的稀疏性。然而，这些先验只能帮助恢复部分频谱，例如靠近高端的频率。为此，我们提出了以下具体问题：（i）与现有的通用先验相比，任何图像类别信息在图像质量恢复方面是否具有优势？（ii）如果存在特定于类别的先验，应如何将其编码到去模糊框架中以恢复衰减的图像频率？在整个工作中，我们设计了一个基于带通滤波器响应的特定于类别的先验，并将其纳入去模糊策略中。更具体地说，我们表明，带通滤波图像及其强度分布的子空间可以作为恢复通用图像先验或特定于类别的示例难以恢复的图像频率的有用先验。我们证明，当我们的图像去模糊框架配备上述先验时，它可以显著优于许多使用通用图像先验或特定于类别的示例的最先进方法。
