In real-world scenarios, image impairments often manifest as composite degradations, presenting a complex interplay of elements such as low light, haze, rain, and snow. Despite this reality, existing restoration methods typically target isolated degradation types, thereby falling short in environments where multiple degrading factors coexist. To bridge this gap, our study proposes a versatile imaging model that consolidates four physical corruption paradigms to accurately represent complex, composite degradation scenarios. In this context, we propose OneRestore, a novel transformer-based framework designed for adaptive, controllable scene restoration. The proposed framework leverages a unique cross-attention mechanism, merging degraded scene descriptors with image features, allowing for nuanced restoration. Our model allows versatile input scene descriptors, ranging from manual text embeddings to automatic extractions based on visual attributes. Our methodology is further enhanced through a composite degradation restoration loss, using extra degraded images as negative samples to fortify model constraints. Comparative results on synthetic and real-world datasets demonstrate OneRestore as a superior solution, significantly advancing the state-of-the-art in addressing complex, composite degradations.

翻译：在现实场景中，图像损伤通常表现为复合退化，呈现出低光照、雾霾、雨雪等多种元素的复杂交织。尽管这一现实存在，现有的修复方法通常针对孤立的退化类型，因此在多种退化因素共存的环境中表现不足。为弥补这一差距，本研究提出了一种通用成像模型，该模型整合了四种物理退化范式，以准确表征复杂的复合退化场景。在此背景下，我们提出了OneRestore——一种基于Transformer的新型框架，专为自适应、可控的场景修复而设计。该框架利用独特的交叉注意力机制，将退化场景描述符与图像特征相融合，从而实现精细化的修复。我们的模型支持多种输入场景描述符，范围从手动文本嵌入到基于视觉属性的自动提取。通过采用复合退化修复损失，并利用额外的退化图像作为负样本以强化模型约束，我们的方法得到了进一步优化。在合成数据集和真实数据集上的对比结果表明，OneRestore是一种优越的解决方案，在应对复杂复合退化方面显著推进了现有技术水平。