Position: Agentic Systems Constitute a Key Component of Next-Generation Intelligent Image Processing
It addresses the problem of brittleness and inflexibility in current image processing methods for researchers and practitioners, but it is incremental as it builds on existing paradigms without introducing new methods or data.
This position paper argues that the image processing community should shift from model-centric development to include agentic system design to address limitations in generalization and adaptability, proposing systems that dynamically select and optimize tools to emulate human expert problem-solving.
This position paper argues that the image processing community should broaden its focus from purely model-centric development to include agentic system design as an essential complementary paradigm. While deep learning has significantly advanced capabilities for specific image processing tasks, current approaches face critical limitations in generalization, adaptability, and real-world problem-solving flexibility. We propose that developing intelligent agentic systems, capable of dynamically selecting, combining, and optimizing existing image processing tools, represents the next evolutionary step for the field. Such systems would emulate human experts' ability to strategically orchestrate different tools to solve complex problems, overcoming the brittleness of monolithic models. The paper analyzes key limitations of model-centric paradigms, establishes design principles for agentic image processing systems, and outlines different capability levels for such agents.