2014 IEEE Conference on Computer Vision and Pattern Recognition

Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake.Even though learning-based deblurring methods exist, they have been limited to the generative case and are computationally expensive.To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality.We address this gap by proposing a discriminative approach for non-blind deblurring.One key challenge is that the blur kernel in use at test time is not known in advance.To address this, we analyze existing approaches that use half-quadratic regularization.From this analysis, we derive a discriminative model cascade for image deblurring.Our cascade model…

• MR
  Microsoft Research