Existing image restoration models are typically designed for specific tasks and struggle to generalize to out-of-sample degradations not encountered during training. While zero-shot methods can address this limitation by fine-tuning model parameters on testing samples, their effectiveness relies on predefined natural priors and physical models of specific degradations. Nevertheless, determining out-of-sample degradations faced in real-world scenarios is always impractical. As a result, it is more desirable to train restoration models with inherent generalization ability. To this end, this work introduces the Out-of-Sample Restoration (OSR) task, which aims to develop restoration models capable of handling out-of-sample degradations. An intuitive solution involves pre-translating out-of-sample degradations to known degradations of restoration models. However, directly translating them in the image space could lead to complex image translation issues. To address this issue, we propose a model reprogramming framework, which translates out-of-sample degradations by quantum mechanic and wave functions. Specifically, input images are decoupled as wave functions of amplitude and phase terms. The translation of out-of-sample degradation is performed by adapting the phase term. Meanwhile, the image content is maintained and enhanced in the amplitude term. By taking these two terms as inputs, restoration models are able to handle out-of-sample degradations without fine-tuning. Through extensive experiments across multiple evaluation cases, we demonstrate the effectiveness and flexibility of our proposed framework. Our codes are available at https://github.com/ddghjikle/Out-of-sample-restoration .

中文翻译：

---

通过模型重新编程推广到样本外退化

现有的图像恢复模型通常是针对特定任务而设计的，并且很难推广到训练期间未遇到的样本外退化。虽然零样本方法可以通过微调测试样本的模型参数来解决这一限制，但其有效性依赖于预定义的自然先验和特定退化的物理模型。然而，确定现实场景中面临的样本外退化始终是不切实际的。因此，更需要训练具有固有泛化能力的恢复模型。为此，这项工作引入了样本外恢复（OSR）任务，旨在开发能够处理样本外退化的恢复模型。直观的解决方案涉及将样本外退化预先转换为恢复模型的已知退化。然而，直接在图像空间中翻译它们可能会导致复杂的图像翻译问题。为了解决这个问题，我们提出了一个模型重编程框架，该框架通过量子力学和波函数来翻译样本外的退化。具体来说，输入图像被解耦为幅度和相位项的波函数。样本外退化的转换是通过调整相位项来执行的。同时，图像内容在幅度项上得到保持和增强。通过将这两项作为输入，恢复模型能够处理样本外的退化，而无需进行微调。通过多个评估案例的广泛实验，我们证明了我们提出的框架的有效性和灵活性。我们的代码可在https://github.com/ddghjikle/Out-of-sample-restoration 。