Abstract
Unsupervised deep image prior (DIP) addresses shortcomings of training data requirements and limited generalization associated with supervised deep learning. The performance of DIP depends on the network architecture and the stopping point of its iterative process. Optimizing these parameters for a new image requires time, restricting DIP application in domains where many images need to be processed. Focusing on fluorescence microscopy data, we hypothesize that similar images share comparable optimal parameter configurations for DIP-based denoising, potentially enabling optimization-free DIP for fluorescence microscopy. We generated a calibration (n=110) and validation set (n=55) of semantically different images from an open-source dataset for a network architecture search targeted towards ideal U-net architectures and stopping points. The calibration set represented our transfer basis. The validation set enabled the assessment of which image similarity criterion yields the best results. We then implemented AUTO-DIP, a pipeline for automatic parameter transfer, and compared it to the originally published DIP configuration (baseline) and a state-of-the-art image-specific variational denoising approach. We show that a parameter transfer from the calibration dataset to a test image based on only image metadata similarity (e.g., microscope type, imaged specimen) leads to similar and better performance than a transfer based on quantitative image similarity measures. AUTO-DIP outperforms the baseline DIP (DIP with original DIP parameters) as well as the variational denoising approaches for several open-source test datasets of varying complexity, particularly for very noisy inputs. Applications to locally acquired fluorescence microscopy images further proved superiority of AUTO-DIP.
Abstract (translated)
无监督深度图像先验（DIP）解决了有监督深度学习中训练数据需求和泛化能力有限的问题。DIP的性能取决于网络架构及其迭代过程的停止点。为了一个新的图像优化这些参数需要时间，这限制了在处理大量图像领域使用DIP的应用范围。针对荧光显微镜数据，我们假设相似的图像具有类似的最佳参数配置，这有可能实现无需优化的DIP应用。为此，我们在一个开源的数据集中生成了一个校准集（n=110）和验证集（n=55），这些集合包含语义上不同的图像，并针对理想的U-net架构和停止点进行了网络结构搜索。校准集代表了我们的迁移基础，而验证集则用于评估哪种图像相似性标准能产生最佳结果。 我们随后实施了一种自动参数转移管道AUTO-DIP，并将其与最初发布的DIP配置（基准线）以及一种最先进的特定图像变分去噪方法进行比较。结果显示，仅基于图像元数据相似性（如显微镜类型、成像标本等），从校准数据集向测试图像传输参数可以实现类似甚至更好的性能，相比基于定量图像相似度测量的转移方法而言。 对于几个不同复杂程度的开源测试数据集，AUTO-DIP优于基准DIP（使用原始DIP参数）以及变分去噪方法，特别是在非常嘈杂的数据输入方面。应用到本地采集的荧光显微镜图像中进一步证明了AUTO-DIP的优势。
URL
https://arxiv.org/abs/2601.12055
