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.

SuperResolution via Wavelet Transform and Advanced Learning Techniques Image superresolution aims to generate a highresolution (HR) image from a lowresolution (LR) input image. In this paper, we propose a deep learningbased approach for image superresolution. We use the wavelet transform to separate the input image into four frequency bands, and train a model for each subband. By processing information from different frequency bands via different CNN models, we can extract features more efficiently and learn better LRtoHR mapping. In addition, we add dense connection to the model to make better use of the internal features in the CNN model. Furthermore, geometric selfensemble is applied in the testing stage to maximize the potential performance. Extensive experiments on four benchmark datasets show the efficiency of the proposed method.