用大模型自动发现显微成像中的图像修复算法，效果优于人工设计。
Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning
- 大模型生成代码并进化出新正则化算法
- 在三个数据集上提升0.26分SSIM和8.3分PSNR
- 可追踪算法演化过程，适合科研与工业图像重建
Ptychography是一种广泛用于高分辨率材料表征的计算成像技术，但高质量重建通常依赖于人工设计的正则化函数。我们提出Ptychi-Evolve，一种基于大语言模型（LLM）的自主框架，用于发现和演化新型正则化算法。该框架结合了LLM驱动的代码生成与进化机制，包括语义引导的交叉与变异。在三个挑战性数据集（X射线集成电路、低剂量apoferritin电子显微镜、存在串扰伪影的多层成像）上的实验表明，所发现的正则化器优于传统方法，实现最高+0.26 SSIM和+8.3~dB PSNR的提升。此外，Ptychi-Evolve记录算法演化谱系与元数据，支持可解释且可复现的分析。
原文摘要 · Abstract (English)
Ptychography is a computational imaging technique widely used for high-resolution materials characterization, but high-quality reconstructions often require the use of regularization functions that largely remain manually designed. We introduce Ptychi-Evolve, an autonomous framework that uses large language models (LLMs) to discover and evolve novel regularization algorithms. The framework combines LLM-driven code generation with evolutionary mechanisms, including semantically-guided crossover and mutation. Experiments on three challenging datasets (X-ray integrated circuits, low-dose electron microscopy of apoferritin, and multislice imaging with crosstalk artifacts) demonstrate that discovered regularizers outperform conventional reconstructions, achieving up to +0.26 SSIM and +8.3~dB PSNR improvements. Besides, Ptychi-Evolve records algorithm lineage and evolution metadata, enabling interpretable and reproducible analysis of discovered regularizers.
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