A new image processing heuristic suggested by optimization experiments – Enhance it before you Lose it

In this paper we report an interesting observation pertaining to Denoising based on the optimization of image processing chains. Although often a goal in itself, Denoising is usually performed in order to minimize the detrimental effects of noise in the subsequent stages of an algorithm. Typically, Denoising is carried out as an early pre-processing stage before other core functions are applied. In the context of optimizing image processing chains for membrane detection, we gathered statistics pertaining to 30 ‘good’ chains, all of which exhibited an average F1 score larger than 90%, and observed that not one was found to use a ‘Denoising function’ as its 1 st step in the processing chain. On the contrary, the optimization process tended to choose Denoising as a middle processing component, and generally selected image enhancement as an earlier component. We conclude, that at least in the context of this membrane detection problem, it is better to enhance information before cleaning (or losing) it.