Image compounding based on independent noise constraint

Image restoration has been extensively studied in the past. But multi-image based restoration/compounding is still surprisingly primitive. It usually starts with weighted averaging of the multiple images followed by single-image based restoration methods, which discards the abundant information hinted in the multiple images that can help the restoration process. In this paper, we utilize the fact that the images are corrupted by independent noise and design a new independence measurement based on the properties of independent random variables. The new independence measurement can be efficiently evaluated and imposed as an energy term into the traditional maximum a posteriori (MAP) framework, compensating to the generative models of signal and noise. It can effectively prevent the signal from being smoothed out as noise and hence dramatically improve the restoration quality and robustness, especially when accurate noise/signal models are difficult to obtain. Experiments on real medical images show very promising results.