摘要： Recent advances in deep learning have facilitated new modalities for transforming the lower resolution image to higher resolution. The generated high resolution image must reconstruct the high frequency details of the image to generate a plausible result. To facilitate feature reuse for the task of super-resolution, we propose residual learning based convolutional neural network architecture. A pixel shuffle operation is performed in the upsampling procedure to mitigate the commonly encountered problem of artifacts in the predicted high resolution image. Our model makes use of a joint loss function consisting of pixel-wise loss and feature loss to learn the mapping from low resolution to its high resolution version. Additionally, our model has the ability to progressively increment to perform multi-scale super-resolution. An extensive experiment is performed to validate our model on the diverse ImageNet dataset. We show the effectiveness of our model through visual comparative assessment as well as quantitative comparative analysis with the state-of-the-art.