A Coarsetofine Classification for Motion Blur Kernel Size Estimation with Cascaded Neural Networks Blur kernel size estimation significantly affects the quality of image deblurring. However, existing methods have a large limitation in estimation accuracy as well as in the adaptability to large kernel sizes. In this paper, we propose a novel blur kernel size estimation method using CNNs. We first convert the 2D kernel size estimation to a 1D classification problem. A cascaded network model using homogeneous networks is then designed to cut the entire estimation space into 38 small intervals to have a more accurate kernel size estimation in a coarsetofine manner. Experiments show that the proposed method has reached a satisfying performance of kernel size estimation with a largely improved adaptability to various blur kernels.

Blind Single Image Superresolution with a Mixture of Deep Networks Abstract   Existing deep neural network based image superresolution (SR) methods are mostly designed for nonblind cases, where the blur kernel used to generate the lowresolution (LR) images is assumed to be known and fixed. However, this assumption does not hold in many real scenarios. Motivated by the observation that SR of LR images generated by different blur kernels are essentially different but also correlated, we propose a mixture model of deep networks, which is capable of clustering SR tasks of different blur kernels into a set of groups. Each group is composed of correlated SR tasks with similar blur kernels and can be effectively handled by a combination of specific networks in the mixture model. To achieve automatic SR tasks clustering and network selection, we model the blur kernel with a latent variable, which is inferred from the input image by an encoder network. Since the groundtruth of the latent variable is unknown in the training stage, we initialize the encoder network by pretraining it on the blur kernel classification task to avoid trivial solutions. To jointly train the mixture model and the encoder network, we further derive a lower bound of the likelihood function, which circumvents the intractability in direct maximum likelihood estimation. Extensive evaluations are performed on benchmark data sets and validate the effectiveness of the proposed method.