text: Human faces are one interesting object class with numerous applications. While significant progress has been made in the generic deblurring problem, existing methods are less effective for blurry face images. The success of the state-of-the-art image deblurring algorithms stems mainly from implicit or explicit restoration of salient edges for kernel estimation. However, existing methods are less effective as only few edges can be restored from blurry face images for kernel estimation. In this paper, we address the problem of deblurring face images by exploiting facial structures. We propose a deblurring algorithm based on an exemplar dataset without using coarse-to-fine strategies or heuristic edge selections. In addition, we develop a convolutional neural network to restore sharp edges from blurry images for deblurring. Extensive experiments against the state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. In addition, we show that the proposed algorithms can be applied to image deblurring for other object classes.
  @cite_5: This paper presents a new method for deblurring photos using a sharp reference example that contains some shared content with the blurry photo. Most previous deblurring methods that exploit information from other photos require an accurately registered photo of the same static scene. In contrast, our method aims to exploit reference images where the shared content may have undergone substantial photometric and non-rigid geometric transformations, as these are the kind of reference images most likely to be found in personal photo albums. Our approach builds upon a recent method for example-based deblurring using non-rigid dense correspondence (NRDC) [ 2011] and extends it in two ways. First, we suggest exploiting information from the reference image not only for blur kernel estimation, but also as a powerful local prior for the non-blind deconvolution step. Second, we introduce a simple yet robust technique for spatially varying blur estimation, rather than assuming spatially uniform blur. Unlike the above previous method, which has proven successful only with simple deblurring scenarios, we demonstrate that our method succeeds on a variety of real-world examples. We provide quantitative and qualitative evaluation of our method and show that it outperforms the state-of-the-art.
summary: Example-based Deblurring. Recently, @cite_5 propose a deblurring method which uses sharp reference examples for guidance. The method requires a reference image with the same contents as the input to obtain dense correspondence for reconstruction. Although it has been shown to deblur specific images well, the assumption of using reference images with same contents limit its application domain. In contrast, the proposed methods do not require the exemplar to have the same or closely similar contents of the input. The blurred face image can be of different identity and background when compared to exemplar images. The proposed methods only require the matched example to have similar structures (in terms of image gradients) for kernel estimation instead of using dense corresponding pixels. As such, the proposed algorithms can be applied to class specific image deblurring with fewer constraints.