  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.
