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、雨雪等多种因素交织的复杂形态。尽管这一现象普遍存在，现有复原方法通常仅针对单一退化类型，因此在多种退化因素共存的场景中表现不足。为弥补这一差距，本研究提出了一种通用的成像模型，该模型整合了四种物理退化范式，以精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、可控的场景复原而设计。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，实现精细化的复原。我们的模型支持多种输入场景描述符，包括手动生成的文本嵌入和基于视觉属性自动提取的特征。通过引入复合退化复原损失函数，并以额外退化图像作为负样本强化模型约束，进一步提升了方法的性能。在合成数据集和真实数据集上的对比实验表明，OneRestore在应对复杂复合退化问题上显著优于现有方法，推动了该领域技术水平的进步。