发表机构
University of Nottingham Ningbo China; University of Nottingham（宁波诺丁汉大学; 诺丁汉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对人工智能时代图像处理的“模型优先”研究弊端，提出六阶段“问题优先”框架，结合超分辨率等案例明确基准与真实问题的差异，呼吁变革研究文化以推动真正的科学技术进步。
AI 中文摘要
现代人工智能极大拓展了图像处理的能力，但强大模型、公开数据集和基准排行榜的易得性，也催生了“模型优先”的研究模式：研究人员日益从现有架构入手，在公开基准上优化，而非从潜在的真实世界成像问题出发。这可能产生令人印象深刻的基准结果，却未必加深对真实问题的理解或解决。本文主张采用“问题优先”方法，区分物理成像问题、解决方案原理、统计估计器与计算实现，同时明确现代人工智能可实现的目标及仍未解决的基础问题。通过超分辨率和低光照增强的案例研究，本文展示了基准数据集如何定义与预期代表的真实问题存在显著差异的任务，以及为何性能提升需在其获得的条件下解读。本文提出了一个六阶段工作流程，将问题表述、图像采集、信息损失分析、假设、歧义性和评估置于模型与数据集选择之前，还提出了更清晰的证据、可复现性、不确定性及最先进性能声明的标准，更根本地呼吁改变研究文化与教育，使未来研究人员学会深入理解成像问题，利用现代人工智能实现真正的科学与技术进步。
英文摘要
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.
