The integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of cost, speed, and form-factor, followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal, superior or alternative data. This strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic imaging. One of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed, critical for capturing fine dynamic biological processes. This approach also offers the prospect of simplifying hardware requirements/complexities, thereby making advanced imaging standards more accessible in terms of cost and/or size. This article provides an in-depth review of the diverse measurement aspects that researchers intentionally impair in their biophotonic setups, including the point spread function, signal-to-noise ratio, sampling density, and pixel resolution. By deliberately compromising these metrics, researchers aim to not only recuperate them through the application of deep learning networks, but also bolster in return other crucial parameters, such as the field-of-view, depth-of-field, and space-bandwidth product. Here, we discuss various biophotonic methods that have successfully employed this strategic approach. These techniques span broad applications and showcase the versatility and effectiveness of deep learning in the context of compromised biophotonic data. Finally, by offering our perspectives on the future possibilities of this rapidly evolving concept, we hope to motivate our readers to explore novel ways of balancing hardware compromises with compensation via AI.

翻译：深度学习技术与生物光子系统的融合为生物成像开辟了新视野。该领域的一个引人注目的趋势是：通过刻意牺牲某些测量指标，以在成本、速度和设备形态方面优化生物成像工具，随后利用基于海量理想、优质或替代数据训练的深度学习模型来补偿由此产生的图像缺陷。这一策略性方法因其提升生物光子成像多维度性能的潜力而日益受到青睐。采用此策略的首要动机之一是追求更高的时间分辨率或更快的成像速度——这对于捕捉精细的动态生物过程至关重要。该方法还能简化硬件要求与复杂度，从而使先进成像标准在成本和尺寸上更具可及性。本文深入综述了研究人员在生物光子系统中刻意损坏的多种测量指标，包括点扩散函数、信噪比、采样密度和像素分辨率。通过主动牺牲这些指标，研究人员不仅期望利用深度学习网络对其进行恢复，更旨在同时增强其他关键参数，如视场、景深和空间带宽积。我们系统讨论了成功运用该策略的各类生物光子方法，这些技术覆盖广阔的应用领域，充分展现了深度学习在处理受损生物光子数据中的灵活性与有效性。最后，基于对这一快速发展概念未来可能性的展望，我们希望激发读者探索硬件权衡与AI补偿之间新型平衡点的创新方案。