A constrained optimization approach to combining multiple non-local means denoising estimates
We solve an optimization problem to combine multiple estimates of a denoised image. A sum of risk and gradient penalties is minimized, and positivity is required. We combine multiple NLM estimates created with different parameter choices. Experimental results show improvement re other NLM methods. By combining several estimates, sensitivity to parameter selection is reduced.
optimization non-local means denoising admm total variation