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Machine Learning: Calibration & Uncertainty QuantificationComputer Vision: Learning & Optimization for CVSearch and Optimization: Learning to Search
Automating Parameter Selection in Deep Image Prior for Fluorescence Microscopy Image Denoising via Similarity-Based Parameter Transfer
🤖 AI Summary
This work addresses the challenge of time-consuming manual hyperparameter tuning in Deep Image Prior (DIP) for fluorescence microscopy image denoising, which hinders its scalability to large-scale processing. To overcome this limitation, we propose AUTO-DIP, a novel unsupervised denoising framework that automatically transfers the optimal U-Net architecture and stopping iteration from a calibration set by leveraging metadata similarity—such as microscope type and sample characteristics—eliminating the need for per-image optimization. Without requiring manual parameter adjustment, AUTO-DIP consistently outperforms the original DIP and state-of-the-art variational methods across multiple public and in-house datasets, demonstrating superior efficiency and generalization, particularly under high-noise conditions.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Deep Image Prior
Fluorescence Microscopy
Image Denoising
Parameter Selection
Unsupervised Learning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Image Prior
Parameter Transfer
Fluorescence Microscopy
Unsupervised Denoising
AUTO-DIP
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