  With exploiting contextual information over large image regions in an
efficient way, the deep convolutional neural network has shown an impressive
performance for single image super-resolution (SR). In this paper, we propose a
deep convolutional network by cascading the well-designed inception-residual
blocks within the deep Laplacian pyramid framework to progressively restore the
missing high-frequency details of high-resolution (HR) images. By optimizing
our network structure, the trainable depth of the proposed network gains a
significant improvement, which in turn improves super-resolving accuracy. With
our network depth increasing, however, the saturation and degradation of
training accuracy continues to be a critical problem. As regard to this, we
propose an effective two-stage training strategy, in which we firstly use
images downsampled from the ground-truth HR images as the optimal objective to
train the inception-residual blocks in each pyramid level with an extremely
high learning rate enabled by gradient clipping, and then the ground-truth HR
images are used to fine-tune all the pre-trained inception-residual blocks for
obtaining the final SR model. Furthermore, we present a new loss function
operating in both image space and local rank space to optimize our network for
exploiting the contextual information among different output components.
Extensive experiments on benchmark datasets validate that the proposed method
outperforms existing state-of-the-art SR methods in terms of the objective
evaluation as well as the visual quality.
