Single image super-resolution via iterative collaborative representation

We propose a new model called iterative collaborative representation (ICR) for image super-resolution (SR). Most of popular SR approaches extract low-resolution (LR) features from the given LR image directly to recover its corresponding high-resolution (HR) features. However, they neglect to utilize the reconstructed HR image for further image SR enhancement. Based on this observation, we extract features from the reconstructed HR image to progressively upscale LR image in an iterative way. In the learning phase, we use the reconstructed and the original HR images as inputs to train the mapping models. These mapping models are then used to upscale the original LR images. In the reconstruction phase, mapping models and LR features extracted from the LR and reconstructed image are then used to conduct image SR in each iteration. Experimental results on standard images demonstrate that our ICR obtains state-of-the-art SR performance quantitatively and visually, surpassing recently published leading SR methods.