Wen Jeffrey, Ahmad Rizwan, Schniter Philip
The Ohio State University, Columbus OH 43210, USA.
Comput Vis ECCV. 2025;15118:182-199. doi: 10.1007/978-3-031-73027-6_11. Epub 2024 Nov 26.
In imaging inverse problems, one seeks to recover an image from missing/corrupted measurements. Because such problems are ill-posed, there is great motivation to quantify the uncertainty induced by the measurement-and-recovery process. Motivated by applications where the recovered image is used for a downstream task, such as soft-output classification, we propose a task-centered approach to uncertainty quantification. In particular, we use conformal prediction to construct an interval that is guaranteed to contain the task output from the true image up to a user-specified probability, and we use the width of that interval to quantify the uncertainty contributed by measurement-and-recovery. For posterior-sampling-based image recovery, we construct locally adaptive prediction intervals. Furthermore, we propose to collect measurements over multiple rounds, stopping as soon as the task uncertainty falls below an acceptable level. We demonstrate our methodology on accelerated magnetic resonance imaging (MRI): https://github.com/jwen307/TaskUQ.
在成像反问题中，人们试图从缺失/损坏的测量数据中恢复图像。由于此类问题是不适定的，因此有很大的动力去量化由测量和恢复过程引起的不确定性。受将恢复的图像用于下游任务（如软输出分类）的应用启发，我们提出了一种以任务为中心的不确定性量化方法。具体而言，我们使用共形预测来构建一个区间，该区间保证以用户指定的概率包含真实图像的任务输出，并且我们使用该区间的宽度来量化测量和恢复所带来的不确定性。对于基于后验采样的图像恢复，我们构建局部自适应预测区间。此外，我们建议进行多轮测量，一旦任务不确定性降至可接受水平以下就停止。我们在加速磁共振成像（MRI）上展示了我们的方法：https://github.com/jwen307/TaskUQ 。
