We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi-Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards challenging regions of the human face such as the eyes and nose. The entire network is trained in an end-to-end fashion. Comprehensive experiments on three different face datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art face deblurring methods. Code is available at: this https URL
Classical image deblurring methods estimate the blur kernel given a blurry image and then apply deconvolution to get the deblurred image. To calculate the blur kernel some techniques assume prior information about the image, and formulate maximum a-posertior (MAP) to obtain the deblurred image. Different priors such sparsity, @math gradient prior, patch prior, manifold Prior, and low-rank have been proposed to obtain regularized reconstructions @cite_45 @cite_11 @cite_35 @cite_42 @cite_27 @cite_16 @cite_9 @cite_1 @cite_31 . @cite_3 estimate the Fourier coefficients of the blur kernel to deblur the image. @cite_20 learn the latent features of the blurry images using the latent features of the clean images to estimate the deblurred image. These CNN-based methods do not perform well compared to the state-of-the-art MAP-based methods for large motion kernels. Recent non-blind image deblurring methods like @cite_15 @cite_43 @cite_27 @cite_30 @cite_29 assume and use some knowledge about the blur kernel. Given the latent blurry input, @cite_29 estimate multiple latent images corresponding to different prior strengths to estimate the final deblurred image. CNN-based techniques like @cite_32 and @cite_25 estimate the blur kernel and address dynamic deblurring.
@cite_30 @cite_35 @cite_15 @cite_9 @cite_42 @cite_1 @cite_29 @cite_3 @cite_32 @cite_43 @cite_27 @cite_45 @cite_31 @cite_16 @cite_25 @cite_20 @cite_11
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Blind motion deblurring methods are primarily responsible for recovering an accurate estimate of the blur kernel. Non-blind deblurring (NBD) methods, on the other hand, attempt to faithfully restore the original image, given the blur estimate. However, NBD is quite susceptible to errors in blur kernel. In this work, we present a convolutional neural network-based approach to handle kernel uncertainty in non-blind motion deblurring. We provide multiple latent image estimates corresponding to different prior strengths obtained from a given blurry observation in order to exploit the complementarity of these inputs for improved learning. To generalize the performance to tackle arbitrary kernel noise, we train our network with a large number of real and synthetic noisy blur kernels. Our network mitigates the effects of kernel noise so as to yield detail-preserving and artifact-free restoration. Our quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method delivers state-of-the-art results. To further underscore the benefits that can be achieved from our network, we propose two adaptations of our method to improve kernel estimates, and image deblurring quality, respectively. Blind image deconvolution, i.e., estimating a blur kernel k and a latent image x from an input blurred image y, is a severely ill-posed problem. In this paper we introduce a new patch-based strategy for kernel estimation in blind deconvolution. Our approach estimates a “trusted” subset of x by imposing a patch prior specifically tailored towards modeling the appearance of image edge and corner primitives. To choose proper patch priors we examine both statistical priors learned from a natural image dataset and a simple patch prior from synthetic structures. Based on the patch priors, we iteratively recover the partial latent image x and the blur kernel k. A comprehensive evaluation shows that our approach achieves state-of-the-art results for uniformly blurred images. We present a new algorithm for removing motion blur from a single image. Our method computes a deblurred image using a unified probabilistic model of both blur kernel estimation and unblurred image restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within this probabilistic model that are inspired by our analysis. These terms include a model of the spatial randomness of noise in the blurred image, as well a new local smoothness prior that reduces ringing artifacts by constraining contrast in the unblurred image wherever the blurred image exhibits low contrast. Finally, we describe an effficient optimization scheme that alternates between blur kernel estimation and unblurred image restoration until convergence. As a result of these steps, we are able to produce high quality deblurred results in low computation time. We are even able to produce results of comparable quality to techniques that require additional input images beyond a single blurry photograph, and to methods that require additional hardware. We present a new method for blind motion deblurring that uses a neural network trained to compute estimates of sharp image patches from observations that are blurred by an unknown motion kernel. Instead of regressing directly to patch intensities, this network learns to predict the complex Fourier coefficients of a deconvolution filter to be applied to the input patch for restoration. For inference, we apply the network independently to all overlapping patches in the observed image, and average its outputs to form an initial estimate of the sharp image. We then explicitly estimate a single global blur kernel by relating this estimate to the observed image, and finally perform non-blind deconvolution with this kernel. Our method exhibits accuracy and robustness close to state-of-the-art iterative methods, while being much faster when parallelized on GPU hardware.