Image Processing from Handcrafted Operators to Foundation Models: A Critical Review of Methods, Applications and Recent Advances
TL;DR
Evaluation in digital image processing is argued for evaluation that is task-aware, distribution-aware and resource-aware, with uncertainty, fairness, calibration and reproducibility treated as core properties rather than optional add-ons.
Abstract
Digital image processing has evolved from deterministic operators designed around explicit assumptions to data-driven systems that learn representations, priors and decision rules from large image collections. This transition has improved performance in restoration, segmentation, compression and visual interpretation, but it has also complicated evaluation, reproducibility and deployment. This critical narrative review examines the field as a connected pipeline rather than as a catalogue of algorithms. It considers image formation and degradation, classical spatial and variational methods, non-local and sparse modelling, convolutional neural networks, vision transformers, diffusion models and emerging foundation models. It also analyses how these approaches are translated into medical imaging, Earth observation, industrial inspection, agriculture and multimedia systems. The evidence indicates that classical methods remain valuable when physical assumptions are credible, data are scarce or interpretability and predictable failure behaviour are important. Learned models are strongest when training and deployment distributions are aligned and task-specific data are sufficiently representative, while hybrid physical and learned approaches can reduce sample requirements and constrain implausible outputs. Transformers and diffusion models broaden context modelling and generative capability, although their computational cost, dependence on pretraining and uncertain behaviour under distribution shift limit universal claims of superiority. Across applications, benchmark gains frequently exceed improvements demonstrated in prospective or operational settings. Image quality metrics, dataset design and reporting practices often fail to capture clinically, environmentally or industrially consequential errors. The review therefore argues for evaluation that is task-aware, distribution-aware and resource-aware, with uncertainty, fairness, calibration and reproducibility treated as core properties rather than optional add-ons. Future progress is likely to depend less on isolated architectural novelty than on credible image-formation models, representative data, robust adaptation, efficient computation and validation against domain outcomes.