Description:

This Master Prompt develops a systematic framework for preparing, correcting and restoring images while preserving the information required for reliable scientific analysis. It addresses image-quality assessment, geometric preparation, intensity normalization, contrast enhancement, illumination and color correction, spatial filtering, frequency-domain processing, denoising, dehazing, deconvolution, super-resolution, modality-specific preprocessing and artifact management.

The framework distinguishes improved visual appearance from validated information recovery, image enlargement from demonstrated resolution improvement, and removal of unwanted variation from accidental removal of meaningful specimen differences. It guides researchers in selecting preprocessing operations according to the imaging mechanism, degradation characteristics, intended measurement and available reference evidence. Particular attention is given to processing order, parameter justification, preservation of raw data, downstream measurement accuracy, uncertainty, computational requirements and reproducibility. Methods mentioned only in an earlier reference list or a paper’s background discussion must not be presented as implemented and validated methods within the uploaded collection.