Recently it has been shown that using diffusion models for inverse problems can lead to remarkable results. However, these approaches require a closed-form expression of the degradation model and can not support complex degradations. To overcome this limitation, we propose a method (INDigo) that combines invertible neural networks (INN) and diffusion models for general inverse problems. Specifically, we train the forward process of INN to simulate an arbitrary degradation process and use the inverse as a reconstruction process. During the diffusion sampling process, we impose an additional data-consistency step that minimizes the distance between the intermediate result and the INN-optimized result at every iteration, where the INN-optimized image is composed of the coarse information given by the observed degraded image and the details generated by the diffusion process. With the help of INN, our algorithm effectively estimates the details lost in the degradation process and is no longer limited by the requirement of knowing the closed-form expression of the degradation model. Experiments demonstrate that our algorithm obtains competitive results compared with recently leading methods both quantitatively and visually. Moreover, our algorithm performs well on more complex degradation models and real-world low-quality images.

翻译：最近的研究表明，将扩散模型应用于逆问题可取得显著成果。然而，这些方法需要降质模型的闭式表达式，且无法处理复杂降质。为克服这一局限，我们提出一种结合可逆神经网络（INN）与扩散模型的新方法（INDigo），用于求解一般逆问题。具体而言，我们训练INN的前向过程模拟任意降质过程，并利用其逆过程作为重建过程。在扩散采样过程中，我们引入额外的数据一致性步骤，在每次迭代中最小化中间结果与INN优化结果之间的距离，其中INN优化图像由观测的降质图像提供的粗粒度信息与扩散过程生成的细节信息共同构成。借助INN，我们的算法能有效估计降质过程中丢失的细节，且不再受限于已知降质模型闭式表达式的约束。实验表明，与近期主流方法相比，本算法在定量指标与视觉效果上均取得具有竞争力的结果。此外，该算法在更复杂的降质模型及真实低质量图像上同样表现优异。