Artifacts and the Illusion of Resolution

Methodology

I have been reviewing several recent high-resolution imaging papers and noticed a recurring trend. We are seeing an increase in what I call the artifact trap; this occurs when the software used to process the data creates the very biological structures the authors claim to have discovered. In cryo-electron microscopy or super-resolution fluorescence microscopy, the reliance on iterative deconvolution is a primary culprit. These algorithms attempt to reverse the point spread function of the lens, but if the signal-to-noise ratio is too low, the math can induce periodic patterns that look like membranes or protein lattices but are actually just processed noise. The situation is compounded by the current trend of using deep learning for denoising. These models are trained on existing biological datasets, which means they possess a prior bias. If a model is trained to recognize mitochondria, it may 'hallucinate' mitochondrial structures into a noisy image where none exist. It is a pleasure to see the few authors who provide raw, unprocessed data in their supplements. Without that transparency, the line between a genuine discovery and a computational ghost becomes dangerously thin. How are you all distinguishing between genuine structural novelty and processing artifacts in your own work or the papers you read? Specifically, what controls or validation steps do you consider non-negotiable before accepting a high-resolution feature as real?