High-quality, high-resolution medical imaging is essential for clinical care. Raman-based biomedical optical imaging uses non-ionizing infrared radiation to evaluate human tissues in real time and is used for early cancer detection, brain tumor diagnosis, and intraoperative tissue analysis. Unfortunately, optical imaging is vulnerable to image degradation due to laser scattering and absorption, which can result in diagnostic errors and misguided treatment. Restoration of optical images is a challenging computer vision task because the sources of image degradation are multi-factorial, stochastic, and tissue-dependent, preventing a straightforward method to obtain paired low-quality/high-quality data. Here, we present Restorative Step-Calibrated Diffusion (RSCD), an unpaired diffusion-based image restoration method that uses a step calibrator model to dynamically determine the number of steps required to complete the reverse diffusion process for image restoration. RSCD outperforms other widely used unpaired image restoration methods on both image quality and perceptual evaluation metrics for restoring optical images. Medical imaging experts consistently prefer images restored using RSCD in blinded comparison experiments and report minimal to no hallucinations. Finally, we show that RSCD improves performance on downstream clinical imaging tasks, including automated brain tumor diagnosis and deep tissue imaging. Our code is available at https://github.com/MLNeurosurg/restorative_step-calibrated_diffusion.
The triangulation of images has become an active research area in recent years for its compressive representation and ease of image processing and visualization. However, little work has been done on how to faithfully recover image intensities from a triangulated mesh of an image, a process also known as image restoration or decoding from meshes. The existing methods such as linear interpolation, least-square interpolation, or interpolation based on radial basis functions (RBFs) work to some extent, but often yield blurred features (edges, corners, etc.). The main reason for this problem is du…
Lightweight Data-Free Denoising for Detail-Preserving Biomedical Image Restoration
School of Computation, Information and Technology, Technical University of Munich（计算信息学院，慕尼黑技术大学）；Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health（海德堡人工智能，海德堡-德国环境健康研究所以及研究中心）；School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）
Institute of Computer Science, University of Tartu（塔尔图大学计算机科学研究所）；Wellcome Sanger Institute（惠康桑格研究所）；PerkinElmer Cellular Technologies（珀金埃尔默细胞技术公司）
Biomedical images are noisy. The imaging equipment itself has physical limitations, and the consequent experimental trade-offs between signal-to-noise ratio, acquisition speed, and imaging depth exacerbate the problem. Denoising is, therefore, an essential part of any image processing pipeline, and convolutional neural networks are currently the method of choice for this task. One popular approach, Noise2Noise, does not require clean ground truth, and instead, uses a second noisy copy as a training target. Self-supervised methods, like Noise2Self and Noise2Void, relax data requirements by lear…
We leverage deep learning techniques to jointly denoise and super-resolve biomedical images acquired with fluorescence microscopy. We develop a deep learning algorithm based on the networks and method described in the recent W2S paper to solve a joint denoising and super-resolution problem. Specifically, we address the restoration of SIM images from widefield images. Our TensorFlow model is trained on the W2S dataset of cell images and is made accessible online in this repository: https://github.com/mchatton/w2s-tensorflow. On test images, the model shows a visually-convincing denoising and in…
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health（亥姆霍兹人工智能平台，亥姆霍兹慕尼黑中心——德国环境健康研究中心）；Georg-August-University Göttingen（哥廷根乔治-奥古斯特大学）；GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔亥姆霍兹海洋研究中心）
Research institute for signals, systems and computational intelligence, sinc(i), FICH-UNL / CONICET（信号、系统与计算智能研究所sinc(i)，国立滨海大学工程与水文系/国家科学技术研究委员会）；École de Technologie Supérieure（高等技术学院）
Despite the astonishing performance of deep-learning based approaches for visual tasks such as semantic segmentation, they are known to produce miscalibrated predictions, which could be harmful for critical decision-making processes. Ensemble learning has shown to not only boost the performance of individual models but also reduce their miscalibration by averaging independent predictions. In this scenario, model diversity has become a key factor, which facilitates individual models converging to different functional solutions. In this work, we introduce Orthogonal Ensemble Networks (OEN), a no…
Image acquisition in many biomedical imaging modalities is corrupted by Poisson noise followed by additive Gaussian noise. While total variation and related regularization methods for solving biomedical inverse problems are known to yield high quality reconstructions, such methods mostly use log-likelihood of either Gaussian or Poisson noise models, and rarely use mixed Poisson-Gaussian (PG) noise model. There is a recent work which deals with exact PG likelihood and totalariation regularization. This method is developed using the log-likelihood of PG model along with total variation regulariz…
Two-Stage Teacher-Student Reliable Prior Learning for Robust Underwater Image Enhancement
College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能与光学电子学院（iOPEN））
Third Military Medical University（第三军医大学）；National University of Defense Technology（国防科技大学）
The recovery of images from the observations that are degraded by a linear operator and further corrupted by Poisson noise is an important task in modern imaging applications such as astronomical and biomedical ones. Gradient-based regularizers involve the popular total variation semi-norm have become standard techniques for Poisson image restoration due to its edge-preserving ability. Various efficient algorithms have been developed for solving the corresponding minimization problem with non-smooth regularization terms. In this paper, motivated by the idea of the alternating direction minimiz…
University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）；University of Salerno（萨勒诺大学）
The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice. Performance is often the only metric used to evaluate segmentations produced by deep neural networks, and calibration is often neglected. However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual infor…
Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling
School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Kunshan Innovation Institute of Xidian University（西安电子科技大学昆山创新研究院）；University of Montenegro（黑山大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Macquarie University（麦考瑞大学）
Over the past decade, deep learning (DL) research in computer vision has been growing rapidly, with many advances in DL-based image analysis methods for biomedical problems. In this work, we introduce MMV_Im2Im, a new open-source python package for image-to-image transformation in bioimaging applications. MMV_Im2Im is designed with a generic image-to-image transformation framework that can be used for a wide range of tasks, including semantic segmentation, instance segmentation, image restoration, and image generation, etc.. Our implementation takes advantage of state-of-the-art machine learni…
PP-Net: A Hybrid Physical-Prior Neural Network for Scattered Light Removal in Biomedical Images on Embedded Devices
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Melbourne（墨尔本大学）；Chinese Academy of Sciences（中国科学院）
Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Electronic Engineering, Tsinghua University（清华大学信息科学与技术国家实验室，电子工程系，清华大学）；Department of Biomedical Engineering, School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University（清华大学生物医学工程系，医学院，传染病诊治协同创新中心，清华大学）
Single-pixel cameras based on the concepts of compressed sensing (CS) leverage the inherent structure of images to retrieve them with far fewer measurements and operate efficiently over a significantly broader spectral range than conventional silicon-based cameras. Recently, photonic time-stretch (PTS) technique facilitates the emergence of high-speed single-pixel cameras. A significant breakthrough in imaging speed of single-pixel cameras enables observation of fast dynamic phenomena. However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts …
MDiff-FMT: Morphology-aware Diffusion Model for Fluorescence Molecular Tomography with Small-scale Datasets
School of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）；School of Biological and Medical Engineering, Beihang University（北京航空航天大学生物与医学工程学院）
