Abstract
Image restoration has traditionally required training specialized models on thousands of paired examples per degradation type. We challenge this paradigm by demonstrating that powerful pre-trained text-conditioned image editing models can be efficiently adapted for multiple restoration tasks through parameter-efficient fine-tuning with remarkably few examples. Our approach fine-tunes LoRA adapters on FLUX.1 Kontext, a state-of-the-art 12B parameter flow matching model for image-to-image translation, using only 16-128 paired images per task, guided by simple text prompts that specify the restoration operation. Unlike existing methods that train specialized restoration networks from scratch with thousands of samples, we leverage the rich visual priors already encoded in large-scale pre-trained editing models, dramatically reducing data requirements while maintaining high perceptual quality. A single unified LoRA adapter, conditioned on task-specific text prompts, effectively handles multiple degradations including denoising, deraining, and dehazing. Through comprehensive ablation studies, we analyze: (i) the impact of training set size on restoration quality, (ii) trade-offs between task-specific versus unified multi-task adapters, (iii) the role of text encoder fine-tuning, and (iv) zero-shot baseline performance. While our method prioritizes perceptual quality over pixel-perfect reconstruction metrics like PSNR/SSIM, our results demonstrate that pre-trained image editing models, when properly adapted, offer a compelling and data-efficient alternative to traditional image restoration approaches, opening new avenues for few-shot, prompt-guided image enhancement. The code to reproduce our results are available at: this https URL
Abstract (translated)
图像恢复传统上需要针对每种退化类型在数千对示例数据上训练专门的模型。我们挑战这一范式，通过展示强大的预训练文本条件图像编辑模型可以仅使用少量配对样本进行高效的参数高效微调（Parameter-Efficient Fine-Tuning），从而适应多种恢复任务。我们的方法是在FLUX.1 Kontext上微调LoRA适配器，FLUX.1 Kontext是用于图到图转换的最先进的120亿参数流匹配模型，每项任务仅使用16至128对图像，并通过简单的文本提示来指导特定的恢复操作。与现有方法不同的是，这些方法从零开始训练专门的恢复网络，需要数千个样本，我们利用大规模预训练编辑模型中已编码的丰富视觉先验知识，在减少数据需求的同时保持高感知质量。 一个单一统一的LoRA适配器，基于任务特定的文本提示，可以有效地处理包括去噪、去雨和去雾在内的多种退化。通过全面的消融研究，我们分析了：（i）训练集大小对恢复质量的影响；（ii）针对特定任务与统一多任务适配器之间的权衡；（iii）文本编码器微调的作用；以及（iv）零样本基线性能。 虽然我们的方法优先考虑感知质量而不是像素完美的重建指标，如PSNR/SSIM，但结果表明，当这些预训练的图像编辑模型得到适当调整时，它们提供了一种有吸引力且数据高效的替代传统图像恢复方法的方法，并为少量样本、提示引导的图像增强开辟了新的途径。 用于重现我们结果的代码可在以下网址获取：[请在此处插入正确的链接]
URL
https://arxiv.org/abs/2601.03391
