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This paper critiques the prevalent model-first approach in image processing research, advocating instead for a problem-first framework that emphasizes a deeper understanding of real-world imaging challenges. By analyzing case studies in super-resolution and low-light enhancement, the authors illustrate how existing benchmarks often misrepresent the complexities of actual problems, leading to misleading performance metrics. The proposed six-stage workflow aims to realign research focus towards problem formulation and evaluation, fostering genuine advancements in the field.
Benchmark-driven results can obscure the true complexities of real-world imaging problems, leading to misguided research priorities.
Modern AI has greatly expanded the capabilities of image processing. However, the ready availability of powerful models, public datasets, and benchmark leaderboards has also en- couraged a model-first research pattern: researchers increasingly begin with an available architecture and optimize it on a public benchmark, rather than beginning with the underlying real-world imaging problem. This can produce impressive benchmark results without necessarily improving our understanding or solution of the real problem. This paper argues for a problem-first approach that distinguishes the physical imaging problem, solution principle, statistical estimator, and computational implementation, while clarifying what modern AI can achieve and which fundamental problems remain unsolved. Through case studies of super- resolution and low-light enhancement, we show how benchmark datasets may define tasks that differ substantially from the real-world problems they are intended to represent, and why performance improvements must be interpreted within the conditions under which they are obtained. We propose a six-stage workflow that places problem formulation, image acquisition, information-loss analysis, assumptions, ambiguity, and evaluation before model and dataset selection. The paper also proposes clearer standards for evidence, reproducibility, uncertainty, and claims of state-of-the-art performance. More fundamentally, it calls for a change in research culture and education so that future researchers learn to understand imaging problems deeply and use modern AI to achieve genuine scientific and technical advancement.
