The realm of classical phase retrieval concerns itself with the arduous task
of recovering a signal from its Fourier magnitude measurements, which are
fraught with inherent ambiguities. A single-exposure intensity measurement is
commonly deemed insufficient for the reconstruction of the primal signal, given
that the absent phase component is imperative for the inverse transformation.
In this work, we present a novel single-shot phase retrieval paradigm from a
fractional Fourier transform (FrFT) perspective, which involves integrating the
FrFT-based physical measurement model within a self-supervised reconstruction
scheme. Specifically, the proposed FrFT-based measurement model addresses the
aliasing artifacts problem in the numerical calculation of Fresnel diffraction,
featuring adaptability to both short-distance and long-distance propagation
scenarios. Moreover, the intensity measurement in the FrFT domain proves highly
effective in alleviating the ambiguities of phase retrieval and relaxing the
previous conditions on oversampled or multiple measurements in the Fourier
domain. Furthermore, the proposed self-supervised reconstruction approach
harnesses the fast discrete algorithm of FrFT alongside untrained neural
network priors, thereby attaining preeminent results. Through numerical
simulations, we demonstrate that both amplitude and phase objects can be
effectively retrieved from a single-shot intensity measurement using the
proposed approach and provide a promising technique for support-free coherent
diffraction imaging.

从分数阶傅里叶变换的角度出发，本文提出一种新颖的单次曝光相位恢复范式，利用分数阶傅里叶变换基于物理测量模型，并结合自监督重建方法，解决了相位恢复中的问题，实现了支持自由相干衍射成像。

从分数傅里叶变换视角的单次相位恢复

Single-shot Phase Retrieval from a Fractional Fourier Transform  Perspective

Electron microscopy (EM) images exhibit anisotropic axial resolution due to
the characteristics inherent to the imaging modality, presenting challenges in
analysis and downstream tasks.In this paper, we propose a diffusion-model-based
framework that overcomes the limitations of requiring reference data or prior
knowledge about the degradation process. Our approach utilizes 2D diffusion
models to consistently reconstruct 3D volumes and is well-suited for highly
downsampled data. Extensive experiments conducted on two public datasets
demonstrate the robustness and superiority of leveraging the generative prior
compared to supervised learning methods. Additionally, we demonstrate our
method's feasibility for self-supervised reconstruction, which can restore a
single anisotropic volume without any training data.

基于扩散模型的框架实现了逆转因成像模态的特性而导致各向异性轴向分辨率的挑战，且无需参考数据或先验知识，通过利用 2D 扩散模型持续重建 3D 体积，在高度下采样数据中表现出优越性，并通过在两个公共数据集上的实验，证明了与监督学习方法相比利用生成先验的鲁棒性和优势。此外，还证实了自监督重建方法可以在无训练数据的情况下恢复单个各向异性体积。

无参考各向同性三维电子显微重建

Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models

Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast,
albeit it is an inherently slow imaging modality. Promising deep learning
methods have recently been proposed to reconstruct accelerated MRI scans.
However, existing methods still suffer from various limitations regarding image
fidelity, contextual sensitivity, and reliance on fully-sampled acquisitions
for model training. To comprehensively address these limitations, we propose a
novel self-supervised deep reconstruction model, named Self-Supervised
Diffusion Reconstruction (SSDiffRecon). SSDiffRecon expresses a conditional
diffusion process as an unrolled architecture that interleaves cross-attention
transformers for reverse diffusion steps with data-consistency blocks for
physics-driven processing. Unlike recent diffusion methods for MRI
reconstruction, a self-supervision strategy is adopted to train SSDiffRecon
using only undersampled k-space data. Comprehensive experiments on public brain
MR datasets demonstrates the superiority of SSDiffRecon against
state-of-the-art supervised, and self-supervised baselines in terms of
reconstruction speed and quality. Implementation will be available at
this https URL

介绍了一种自监督深度 MRI 重建模型，名为 SSDiffRecon，该模型在使用不完整采样的 k 空间数据的情况下通过条件扩散过程实现了快速和高质量的图像重建。