Problem
Research questions and friction points this paper is trying to address.
Image Processing
Artificial Intelligence
Benchmarking
Research Methodology
Scientific Progress
Rethinking Image Processing for the Age of AI: A Problem-First Framework for Scientific Progress
🤖 AI Summary
本文针对图像处理领域过度依赖模型优化而忽视实际问题的现象，提出了一种以问题为导向的研究框架，通过区分物理成像问题、解决方案原理等步骤，并强调现代AI的应用边界与未解决问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
problem-first approach
six-stage workflow
imaging problem understanding
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