A Progressive Network Based on Residual Multiscale Aggregation for Image SuperResolution In recent years, with the rapid development of deep learning, single image superresolution based on convolution neural network has achieved extensive research. However, most CNNbased method has difficulty in training and obtaining high quality images for large scale factors. To address these issues, we propose a network, which reconstructs HR images at large factors by progressively performing 2xc3x97 SR on the input from the previous level. At each level, cascaded residual multiscale aggregation blocks are used. The Uresidual unit in it makes network simplifier and training easier without performance degradation. The multiscale dilated unit in it provides more comprehensive information for image reconstruction. Before upsampling, the channel attention mechanism is adopted to recalibrate features. We train the network with twostage training strategy which could accelerate the convergence and achieve better performance. Experiment results show that our proposed method is superior to the stateoftheart methods on most datasets, especially on Urban100.

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.