Illumination-invariance and nonlocal means based super resolution

In this paper, we propose a novel algorithm for multi-frame super resolution (SR) with illumination-invariance. Traditional multi-frame SR methods fail to handle images with illumination changes, so in our approach, we adjust the contrast between different search windows and select proper candidate patches to take full advantage of intensity information. We simplify Speed Up Robust Features to get local structure information and incorporate the local structure information into similarity measurement, which does not change significantly in complex illumination situation. By combining intensity and structure information in a proper way, our algorithm Illumination-Invariant Nonlocal Means SR could find more potential similar patches in frames where there are illumination changes than Nonlocal Means SR (NLM SR). Experimental results demonstrate that our algorithm has better performance both in objective and subjective perception with complex illumination conditions and is comparable to NLM SR in stable illumination situation.