METHOD FOR PERFORMING SUPER-RESOLUTION ON SINGLE IMAGES AND APPARATUS FOR PERFORMING SUPER-RESOLUTION ON SINGLE IMAGES

摘要

A noise-aware single-image super-resolution (SI-SR) method and apparatus automatically cancels additive noise while adding detail learnt from lower scale of an input image. In contrast to common SI-SR techniques, the input image is not necessarily assumed to be a clean source of examples. Instead, a recent and efficient in-place cross-scale self-similarity prior is exploited for both learning fine detail examples to complement the interpolation-based upscaled image patches and reducing image noise. Experimental results show a promising performance, despite of the relatively simple algorithm. Both objective and subjective evaluations show large quality improvements when upscaling images immersed in noise.