In the image acquisition process, various forms of degradation, including noise, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from degraded versions, numerous specialized restoration methods have been developed, each targeting a specific type of degradation. Recently, all-in-one algorithms have garnered significant attention by addressing different types of degradations within a single model without requiring prior information of the input degradation type. However, these methods purely operate in the spatial domain and do not delve into the distinct frequency variations inherent to different degradation types. To address this gap, we propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task. Specifically, we first mine low- and high-frequency information from the input features, guided by the adaptively decoupled spectra of the degraded image. The extracted features are then modulated by a bidirectional operator to facilitate interactions between different frequency components. Finally, the modulated features are merged into the original input for a progressively guided restoration. With this approach, the model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on different image restoration tasks, including denoising, dehazing, deraining, motion deblurring, and low-light image enhancement. Our code is available at https://github.com/c-yn/AdaIR.

翻译：在图像采集过程中，常引入噪声、雾霾、雨痕等多种退化形式。这些退化通常源于相机的固有局限性或不利的环境条件。为从退化版本中恢复清晰图像，研究者已开发出众多专用于特定退化类型的复原方法。近年来，全合一算法通过单一模型处理多种退化类型而无需输入退化类型的先验信息，引起了广泛关注。然而，这类方法仅在空间域操作，未能深入挖掘不同退化类型固有的独特频率变化。为填补这一空白，我们提出一种基于频率挖掘与调制的自适应全合一图像复原网络。本方法的动机源于对不同退化类型在不同频率子带上影响图像内容的观察，因而每种复原任务需要采取差异化的处理策略。具体而言，我们首先在退化图像自适应解耦频谱的引导下，从输入特征中挖掘低频与高频信息；随后通过双向算子对提取的特征进行调制，以促进不同频率分量间的交互；最终将调制后的特征融合回原始输入，实现渐进式引导复原。通过该方法，模型能够根据不同输入退化强化信息丰富的频率子带，从而实现自适应重建。大量实验表明，所提方法在图像去噪、去雾、去雨、运动去模糊及低光照增强等不同图像复原任务中均达到最优性能。我们的代码已开源至https://github.com/c-yn/AdaIR。