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.

中文翻译：

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具有特定类别先验的图像去模糊

图像去模糊的一个基本问题是要可靠地恢复已被模糊内核抑制的独特空间频率。为了解决这个问题，现有的图像去模糊技术通常依赖于一般的图像先验，例如包括图像梯度和边缘在内的显着特征的稀疏性。但是，这些先验仅有助于恢复部分频谱，例如高端附近的频率。为此，我们提出以下具体问题：（i）是否有任何图像类别信息在图像质量恢复方面比现有的先验先验优势？（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，我们根据带通滤波器的响应设计特定于类别的先验，并将其纳入去模糊策略。更具体地说，我们表明，带通滤波图像的子空间及其强度分布可作为有用的先验，用于恢复难以通过通用图像先验恢复的图像频率。我们证明，当配备了上述先验条件后，我们的图像去模糊框架将大大优于使用通用先验图像或特定于类的示例的许多最新方法。