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Cool - that's TV on RGB. There may be improvements on the channel selection in the literature in terms of "selectivity" for real images - i.e. HSV or something else. That might be an easy change that would bump your performance.

Also you could apply some nonlinear function to calculated TV to reflect your rejection penalties - i.e. if it's greater than x make the energy arbitrarily high. The thing is you would like to then reject the whole family of solutions that had that adjacency in your annealing, which doesn't seem super straightforward to me.

The other thing I see at the end is that it hits a local optima of multiple subsets of correct adjacencies (up to reflection) but there's no way to permute those subsets to get a better solution, as the temperature is too low. You'd almost want to "group" those subsets and then rerun on the 8 or so segments just over permutation and reflection. On the other hand, it does suggest that a "greedy" approach gets you pretty close - finding a locally optimal solution solves part of the global optimization. Then you can reduce the final problem to an exhaustive search of a much smaller space.




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