Untrained Physics-based Deep Learning (DL) methods for digital holography
have gained significant attention due to their benefits, such as not requiring
an annotated training dataset, and providing interpretability since utilizing
the governing laws of hologram formation. However, they are sensitive to the
hard-to-obtain precise object distance from the imaging plane, posing the
$\textit{Autofocusing}$ challenge. Conventional solutions involve
reconstructing image stacks for different potential distances and applying
focus metrics to select the best results, which apparently is computationally
inefficient. In contrast, recently developed DL-based methods treat it as a
supervised task, which again needs annotated data and lacks generalizability.
To address this issue, we propose $\textit{reverse-attention loss}$, a weighted
sum of losses for all possible candidates with learnable weights. This is a
pioneering approach to addressing the Autofocusing challenge in untrained
deep-learning methods. Both theoretical analysis and experiments demonstrate
its superiority in efficiency and accuracy. Interestingly, our method presents
a significant reconstruction performance over rival methods (i.e. alternating
descent-like optimization, non-weighted loss integration, and random distance
assignment) and even is almost equal to that achieved with a precisely known
object distance. For example, the difference is less than 1dB in PSNR and 0.002
in SSIM for the target sample in our experiment.

提出了一种解决无训练深度学习方法中自动对焦挑战的创新方法，基于物理学原理的数字全息图像重建，通过可学习权重的反向关注损失取得了更高的效率和准确性。

利用反向注意损失增强数字全息图重建的未经训练的基于物理驱动的深度学习模型，对于具有不确定距离的情况

Enhancing Digital Hologram Reconstruction Using Reverse-Attention Loss  for Untrained Physics-Driven Deep Learning Models with Uncertain Distance

Image corruption by motion artifacts is an ingrained problem in Magnetic
Resonance Imaging (MRI). In this work, we propose a neural network-based
regularization term to enhance Autofocusing, a classic optimization-based
method to remove motion artifacts. The method takes the best of both worlds:
the optimization-based routine iteratively executes the blind demotion and deep
learning-based prior penalizes for unrealistic restorations and speeds up the
convergence. We validate the method on three models of motion trajectories,
using synthetic and real noisy data. The method proves resilient to noise and
anatomic structure variation, outperforming the state-of-the-art demotion
methods.

本研究提出了一种基于神经网络的正则化项来增强自动对焦的方法，以解决 MRI 中由运动造成的图像失真问题，并通过使用合成和真实噪声数据验证其有效性，表明该方法对噪声和解剖结构变化具有鲁棒性，优于现有的降噪方法。

自动对焦 +: 磁共振成像中噪声鲁棒运动校正

Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging

Data-driven design is making headway into a number of application areas,
including protein, small-molecule, and materials engineering. The design goal
is to construct an object with desired properties, such as a protein that binds
to a therapeutic target, or a superconducting material with a higher critical
temperature than previously observed. To that end, costly experimental
measurements are being replaced with calls to high-capacity regression models
trained on labeled data, which can be leveraged in an in silico search for
design candidates. However, the design goal necessitates moving into regions of
the design space beyond where such models were trained. Therefore, one can ask:
should the regression model be altered as the design algorithm explores the
design space, in the absence of new data? Herein, we answer this question in
the affirmative. In particular, we (i) formalize the data-driven design problem
as a non-zero-sum game, (ii) develop a principled strategy for retraining the
regression model as the design algorithm proceeds---what we refer to as
autofocusing, and (iii) demonstrate the promise of autofocusing empirically.

数据驱动设计正在广泛应用于蛋白质、小分子和材料工程等领域。然而，设计目标需要进入超出模型训练范围的设计空间， 因此需要针对式样寻找新的候选设计。本文提出了一种自动对焦方法来调整回归模型，以适应训练范围之外的新式样，从而更好地实现数据驱动设计目标。